https://satijalab.org/signac/articles/merging.html https://satijalab.org/signac/articles/integrate_atac.html https://satijalab.org/signac/articles/pbmc_multiomic.html
merge peaks from all samples
recount
merge
(if necessary) integrate
motif enrichment analysis
TF footprint
load RNA-seq
match cell barcodes - actually, cellranger-arc gives to GEX and ATAC the GEX barcodes
do integrated processing
co-accessibility analysis - not done, requires Monocle 3
Setup chunk
Setup reticulate
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")
[1] "/nas/groups/treutlein/USERS/tomasgomes/projects/pallium_evo"
Load libraries
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)
[1] FALSE
use_python(Sys.which("python"))
py_config()
python: /home/tpires/bin/miniconda3/bin/python
libpython: /home/tpires/bin/miniconda3/lib/libpython3.8.so
pythonhome: /home/tpires/bin/miniconda3:/home/tpires/bin/miniconda3
version: 3.8.3 (default, May 19 2020, 18:47:26) [GCC 7.3.0]
numpy: /home/tpires/bin/miniconda3/lib/python3.8/site-packages/numpy
numpy_version: 1.18.5
NOTE: Python version was forced by RETICULATE_PYTHON
Mitochondrial genes
library(Pando)
library(foreach) # needed for Pando
library(igraph) # for Pando network analysis
# multiome factor analysis
library(MOFA2)
Attaching package: ‘MOFA2’
The following object is masked from ‘package:stats’:
predict
source("scripts/MOFA_toSeurat.R") #might not be needed with MOFA2 dev version
# getting GTF
# remotes::install_github("alyssafrazee/ballgown")
library(ballgown)
Attaching package: ‘ballgown’
The following object is masked from ‘package:data.table’:
last
The following object is masked from ‘package:ggplot2’:
expr
The following object is masked from ‘package:base’:
structure
# plotting
library(ggplot2)
library(ggnetwork)
library(ggrepel)
library(patchwork)
Read in axolotl features
mtgenes = c("COX1", "COX2", "COX3", "ATP6", "ND1", "ND5", "CYTB", "ND2", "ND4",
"ATP8", "MT-CO1", "COI", "LOC9829747")
mtgenes = c(mtgenes, paste0("MT", mtgenes), paste0("MT-", mtgenes))
Read in data
gtf_axolotl = "/local1/USERS/tomasgomes/multiome_analysis/AmexT_v47.FULL_corr_chr_cut.gtf"
granges_axolotl = ballgown::gffReadGR(gtf_axolotl)
connection is not positioned at the start of the file, rewinding it
# adding a gene biotype, as that's necessary for TSS metaprofile
granges_axolotl$gene_biotype = "protein_coding"
# with need to add the "proper" gene name
basedir = "/links/groups/treutlein/USERS/tomasgomes/gene_refs/axolotl/Amex_T_v47/"
gene_name_match = read.table(paste0(basedir, "AmexT_v47.FULL_t2g_note.txt"), sep = "\t")[,2:3]
gene_name_match = gene_name_match[!duplicated(gene_name_match$V2), ]
rownames(gene_name_match) = gene_name_match$V2
newgenenames = gene_name_match[granges_axolotl$gene_id,2]
granges_axolotl$gene_name = newgenenames
Merge peaks
samples = c("a1_1", "a1_2", "a3_1", "a3_2")
#data_dir = "/links/groups/treutlein/USERS/tomasgomes/data/axolotl/"
data_dir = "/local1/USERS/tomasgomes/multiome_analysis/"
counts_l = list()
frags_l = list()
peaks_l = list()
for(s in samples){
counts_l[[s]] = Read10X_h5(filename = paste0(data_dir, s, "_ARC/outs/filtered_feature_bc_matrix.h5"))
frags_file = paste0(data_dir, s, "_ARC/outs/atac_fragments.tsv.gz")
peaks = read.table(paste0(data_dir, s, "_ARC/outs/atac_peaks.bed"),
col.names = c("chr", "start", "end"))
peaks_l[[s]] = makeGRangesFromDataFrame(peaks)
frags_l[[s]] = CreateFragmentObject(path = frags_file, cells = colnames(counts_l[[s]]$Peaks))
}
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 7341 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 9035 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 7835 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 8060 cell barcodes
Make all individual Seurats with common peaks
# Create a unified set of peaks to quantify in each dataset
combined.peaks = reduce(x = c(peaks_l$a1_1, peaks_l$a1_2, peaks_l$a3_1, peaks_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined.peaks)
combined.peaks = combined.peaks[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined.peaks = restrict(combined.peaks, start = 1)
combined.peaks
GRanges object with 631645 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1ps1 71686-72012 *
[2] chr1ps1 74070-74148 *
[3] chr1ps1 141663-141901 *
[4] chr1ps1 144157-144549 *
[5] chr1ps1 146038-147572 *
... ... ... ...
[631641] chr14q 436366888-436367192 *
[631642] chr14q 436386394-436387106 *
[631643] chr14q 436388915-436389695 *
[631644] chr14q 436390286-436390344 *
[631645] chr14q 436403784-436405280 *
-------
seqinfo: 66 sequences from an unspecified genome; no seqlengths
Getting MT peaks and calculating MT%
srat_atac_l = list()
for(s in names(frags_l)){
feat = FeatureMatrix(fragments = frags_l[[s]], features = combined.peaks,
cells = colnames(counts_l[[s]]$Peaks))
ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
min.features = 100, annotation = granges_axolotl)
srat_atac_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
srat_atac_l[[s]]$dataset = s
srat_atac_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~35m 26s
|++ | 3 % ~34m 08s
|++ | 4 % ~32m 34s
|+++ | 5 % ~31m 37s
|++++ | 6 % ~31m 05s
|++++ | 8 % ~31m 07s
|+++++ | 9 % ~30m 38s
|++++++ | 10% ~30m 14s
|++++++ | 11% ~29m 47s
|+++++++ | 13% ~29m 26s
|+++++++ | 14% ~29m 18s
|++++++++ | 15% ~28m 48s
|+++++++++ | 16% ~28m 18s
|+++++++++ | 18% ~27m 54s
|++++++++++ | 19% ~27m 27s
|+++++++++++ | 20% ~26m 53s
|+++++++++++ | 22% ~26m 31s
|++++++++++++ | 23% ~26m 02s
|+++++++++++++ | 24% ~25m 33s
|+++++++++++++ | 25% ~25m 06s
|++++++++++++++ | 27% ~24m 36s
|++++++++++++++ | 28% ~24m 12s
|+++++++++++++++ | 29% ~23m 47s
|++++++++++++++++ | 30% ~23m 25s
|++++++++++++++++ | 32% ~23m 03s
|+++++++++++++++++ | 33% ~22m 37s
|++++++++++++++++++ | 34% ~22m 13s
|++++++++++++++++++ | 35% ~21m 48s
|+++++++++++++++++++ | 37% ~21m 23s
|+++++++++++++++++++ | 38% ~20m 58s
|++++++++++++++++++++ | 39% ~20m 34s
|+++++++++++++++++++++ | 41% ~20m 08s
|+++++++++++++++++++++ | 42% ~19m 42s
|++++++++++++++++++++++ | 43% ~19m 15s
|+++++++++++++++++++++++ | 44% ~18m 47s
|+++++++++++++++++++++++ | 46% ~18m 21s
|++++++++++++++++++++++++ | 47% ~18m 04s
|+++++++++++++++++++++++++ | 48% ~17m 39s
|+++++++++++++++++++++++++ | 49% ~17m 13s
|++++++++++++++++++++++++++ | 51% ~16m 48s
|++++++++++++++++++++++++++ | 52% ~16m 21s
|+++++++++++++++++++++++++++ | 53% ~15m 57s
|++++++++++++++++++++++++++++ | 54% ~15m 30s
|++++++++++++++++++++++++++++ | 56% ~15m 04s
|+++++++++++++++++++++++++++++ | 57% ~14m 37s
|++++++++++++++++++++++++++++++ | 58% ~14m 12s
|++++++++++++++++++++++++++++++ | 59% ~13m 45s
|+++++++++++++++++++++++++++++++ | 61% ~13m 18s
|++++++++++++++++++++++++++++++++ | 62% ~12m 54s
|++++++++++++++++++++++++++++++++ | 63% ~12m 27s
|+++++++++++++++++++++++++++++++++ | 65% ~12m 01s
|+++++++++++++++++++++++++++++++++ | 66% ~11m 36s
|++++++++++++++++++++++++++++++++++ | 67% ~11m 10s
|+++++++++++++++++++++++++++++++++++ | 68% ~10m 44s
|+++++++++++++++++++++++++++++++++++ | 70% ~10m 18s
|++++++++++++++++++++++++++++++++++++ | 71% ~09m 53s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09m 27s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09m 01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08m 35s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08m 09s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07m 43s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07m 18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06m 52s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06m 27s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~06m 01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05m 35s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05m 10s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04m 44s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04m 18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03m 52s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03m 26s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03m 00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02m 35s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02m 09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01m 43s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01m 17s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~51s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=33m 53s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~34m 53s
|++ | 3 % ~34m 26s
|++ | 4 % ~33m 14s
|+++ | 5 % ~32m 18s
|++++ | 6 % ~31m 38s
|++++ | 8 % ~31m 26s
|+++++ | 9 % ~31m 05s
|++++++ | 10% ~30m 42s
|++++++ | 11% ~30m 06s
|+++++++ | 13% ~29m 46s
|+++++++ | 14% ~29m 10s
|++++++++ | 15% ~28m 55s
|+++++++++ | 16% ~28m 23s
|+++++++++ | 18% ~27m 56s
|++++++++++ | 19% ~27m 32s
|+++++++++++ | 20% ~27m 03s
|+++++++++++ | 22% ~26m 40s
|++++++++++++ | 23% ~26m 16s
|+++++++++++++ | 24% ~25m 45s
|+++++++++++++ | 25% ~25m 23s
|++++++++++++++ | 27% ~24m 53s
|++++++++++++++ | 28% ~24m 28s
|+++++++++++++++ | 29% ~24m 02s
|++++++++++++++++ | 30% ~23m 37s
|++++++++++++++++ | 32% ~23m 18s
|+++++++++++++++++ | 33% ~22m 51s
|++++++++++++++++++ | 34% ~22m 27s
|++++++++++++++++++ | 35% ~21m 59s
Nucleosome and TSS signal (serve as QC metrics)
tss_atac_plts = list()
frag_atac_plts = list()
for(n in names(srat_atac_l)){
# compute nucleosome signal score per cell
srat_atac_l[[n]] = NucleosomeSignal(object = srat_atac_l[[n]])
# compute TSS enrichment score per cell
srat_atac_l[[n]] = TSSEnrichment(object = srat_atac_l[[n]], fast = FALSE)
srat_atac_l[[n]]$high.tss = ifelse(srat_atac_l[[n]]$TSS.enrichment > 2, 'High', 'Low')
tss_atac_plts[[n]] = TSSPlot(srat_atac_l[[n]], group.by = 'high.tss') + NoLegend()
srat_atac_l[[n]]$nucleosome_group = ifelse(srat_atac_l[[n]]$nucleosome_signal > 4, 'NS > 2', 'NS < 4')
# this is veeery slow (~30-40min)
frag_atac_plts[[n]] = FragmentHistogram(object = srat_atac_l[[n]], group.by = 'nucleosome_group')
}
Save data
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l_raw.RDS")
Peak calling with MACS
macs_l = list()
for(n in names(srat_atac_l)){
print(n)
srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = 5)
srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
cordat = DepthCor(srat_atac_l[[n]], n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
print(dims_use)
srat_atac_l[[n]] = FindNeighbors(object = srat_atac_l[[n]], reduction = 'lsi', dims = dims_use,
force.recalc = T, graph.name = "thegraph")
srat_atac_l[[n]] = FindClusters(object = srat_atac_l[[n]], verbose = FALSE, algorithm = 3,
graph.name = "thegraph", resolution = 2)
macs_l[[n]] = CallPeaks(object = srat_atac_l[[n]], group.by = "thegraph_res.2",
macs2.path = "~/bin/miniconda3/envs/macs2env/bin/macs3",
effective.genome.size = 2.0e+10, verbose = F)
}
How do new peaks compare with the original?
# Create a unified set of peaks to quantify in each dataset
combined_macs = reduce(x = c(macs_l$a1_1, macs_l$a1_2, macs_l$a3_1, macs_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined_macs)
combined_macs = combined_macs[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined_macs = restrict(combined_macs, start = 1)
combined_macs
boxplot(x = list("MACS" = log10(width(combined_macs)), "Original" = log10(width(combined.peaks))))
Generate new counts and objects
# Create a unified set of peaks to quantify in each dataset
combined_macs = reduce(x = c(macs_l$a1_1, macs_l$a1_2, macs_l$a3_1, macs_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined_macs)
combined_macs = combined_macs[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined_macs = restrict(combined_macs, start = 1)
combined_macs
GRanges object with 135606 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1ps1 186559-186766 *
[2] chr1ps1 276267-276727 *
[3] chr1ps1 276830-277150 *
[4] chr1ps1 2111397-2111909 *
[5] chr1ps1 2286644-2286846 *
... ... ... ...
[135602] chr14q 436116542-436117020 *
[135603] chr14q 436167769-436168171 *
[135604] chr14q 436386504-436386747 *
[135605] chr14q 436389008-436389318 *
[135606] chr14q 436403902-436404556 *
-------
seqinfo: 66 sequences from an unspecified genome; no seqlengths
boxplot(x = list("MACS" = log10(width(combined_macs)), "Original" = log10(width(combined.peaks))))
Getting MT peaks and calculating MT%
srat_macs_l = list()
for(s in names(frags_l)){
print(s)
feat = FeatureMatrix(fragments = frags_l[[s]], features = combined_macs,
cells = colnames(counts_l[[s]]$Peaks))
ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
min.features = 100, annotation = granges_axolotl)
srat_macs_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
srat_macs_l[[s]]$dataset = s
srat_macs_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}
[1] "a1_1"
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~09m 01s
|++ | 3 % ~08m 15s
|+++ | 4 % ~07m 54s
|+++ | 6 % ~07m 43s
|++++ | 7 % ~07m 39s
|+++++ | 9 % ~07m 30s
|++++++ | 10% ~07m 24s
|++++++ | 12% ~07m 14s
|+++++++ | 13% ~07m 02s
|++++++++ | 15% ~06m 57s
|+++++++++ | 16% ~06m 47s
|+++++++++ | 18% ~06m 40s
|++++++++++ | 19% ~06m 33s
|+++++++++++ | 21% ~06m 25s
|++++++++++++ | 22% ~06m 17s
|++++++++++++ | 24% ~06m 10s
|+++++++++++++ | 25% ~06m 01s
|++++++++++++++ | 26% ~05m 54s
|++++++++++++++ | 28% ~05m 47s
|+++++++++++++++ | 29% ~05m 42s
|++++++++++++++++ | 31% ~05m 35s
|+++++++++++++++++ | 32% ~05m 28s
|+++++++++++++++++ | 34% ~05m 21s
|++++++++++++++++++ | 35% ~05m 14s
|+++++++++++++++++++ | 37% ~05m 07s
|++++++++++++++++++++ | 38% ~05m 00s
|++++++++++++++++++++ | 40% ~04m 53s
|+++++++++++++++++++++ | 41% ~04m 46s
|++++++++++++++++++++++ | 43% ~04m 38s
|+++++++++++++++++++++++ | 44% ~04m 31s
|+++++++++++++++++++++++ | 46% ~04m 24s
|++++++++++++++++++++++++ | 47% ~04m 17s
|+++++++++++++++++++++++++ | 49% ~04m 10s
|+++++++++++++++++++++++++ | 50% ~04m 03s
|++++++++++++++++++++++++++ | 51% ~03m 57s
|+++++++++++++++++++++++++++ | 53% ~03m 49s
|++++++++++++++++++++++++++++ | 54% ~03m 42s
|++++++++++++++++++++++++++++ | 56% ~03m 35s
|+++++++++++++++++++++++++++++ | 57% ~03m 28s
|++++++++++++++++++++++++++++++ | 59% ~03m 20s
|+++++++++++++++++++++++++++++++ | 60% ~03m 13s
|+++++++++++++++++++++++++++++++ | 62% ~03m 06s
|++++++++++++++++++++++++++++++++ | 63% ~02m 59s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 52s
|++++++++++++++++++++++++++++++++++ | 66% ~02m 44s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 37s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 30s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 23s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 16s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01m 54s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 47s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 40s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 33s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 26s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 19s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~57s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~50s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~36s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08m 09s
[1] "a1_2"
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~08m 47s
|++ | 3 % ~08m 17s
|+++ | 4 % ~07m 54s
|+++ | 6 % ~07m 39s
|++++ | 7 % ~07m 35s
|+++++ | 9 % ~07m 27s
|++++++ | 10% ~07m 18s
|++++++ | 12% ~07m 12s
|+++++++ | 13% ~07m 01s
|++++++++ | 15% ~06m 59s
|+++++++++ | 16% ~06m 48s
|+++++++++ | 18% ~06m 41s
|++++++++++ | 19% ~06m 33s
|+++++++++++ | 21% ~06m 28s
|++++++++++++ | 22% ~06m 23s
|++++++++++++ | 24% ~06m 15s
|+++++++++++++ | 25% ~06m 08s
|++++++++++++++ | 26% ~06m 01s
|++++++++++++++ | 28% ~05m 54s
|+++++++++++++++ | 29% ~05m 47s
|++++++++++++++++ | 31% ~05m 40s
|+++++++++++++++++ | 32% ~05m 33s
|+++++++++++++++++ | 34% ~05m 25s
|++++++++++++++++++ | 35% ~05m 18s
|+++++++++++++++++++ | 37% ~05m 10s
|++++++++++++++++++++ | 38% ~05m 03s
|++++++++++++++++++++ | 40% ~04m 57s
|+++++++++++++++++++++ | 41% ~04m 49s
|++++++++++++++++++++++ | 43% ~04m 42s
|+++++++++++++++++++++++ | 44% ~04m 36s
|+++++++++++++++++++++++ | 46% ~04m 28s
|++++++++++++++++++++++++ | 47% ~04m 22s
|+++++++++++++++++++++++++ | 49% ~04m 14s
|+++++++++++++++++++++++++ | 50% ~04m 07s
|++++++++++++++++++++++++++ | 51% ~03m 60s
|+++++++++++++++++++++++++++ | 53% ~03m 53s
|++++++++++++++++++++++++++++ | 54% ~03m 46s
|++++++++++++++++++++++++++++ | 56% ~03m 38s
|+++++++++++++++++++++++++++++ | 57% ~03m 31s
|++++++++++++++++++++++++++++++ | 59% ~03m 24s
|+++++++++++++++++++++++++++++++ | 60% ~03m 17s
|+++++++++++++++++++++++++++++++ | 62% ~03m 10s
|++++++++++++++++++++++++++++++++ | 63% ~03m 03s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 56s
|++++++++++++++++++++++++++++++++++ | 66% ~02m 48s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 41s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 34s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 27s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 12s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01m 57s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 50s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 43s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 35s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 28s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 21s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~59s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~51s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~37s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08m 14s
[1] "a3_1"
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~09m 09s
|++ | 3 % ~08m 26s
|+++ | 4 % ~08m 12s
|+++ | 6 % ~08m 12s
|++++ | 7 % ~08m 06s
|+++++ | 9 % ~07m 56s
|++++++ | 10% ~07m 47s
|++++++ | 12% ~07m 41s
|+++++++ | 13% ~07m 33s
|++++++++ | 15% ~07m 32s
|+++++++++ | 16% ~07m 25s
|+++++++++ | 18% ~07m 22s
|++++++++++ | 19% ~07m 12s
|+++++++++++ | 21% ~07m 03s
|++++++++++++ | 22% ~06m 59s
|++++++++++++ | 24% ~06m 55s
|+++++++++++++ | 25% ~06m 48s
|++++++++++++++ | 26% ~06m 40s
|++++++++++++++ | 28% ~06m 33s
|+++++++++++++++ | 29% ~06m 26s
|++++++++++++++++ | 31% ~06m 18s
|+++++++++++++++++ | 32% ~06m 10s
|+++++++++++++++++ | 34% ~06m 02s
|++++++++++++++++++ | 35% ~05m 53s
|+++++++++++++++++++ | 37% ~05m 44s
|++++++++++++++++++++ | 38% ~05m 38s
|++++++++++++++++++++ | 40% ~05m 31s
|+++++++++++++++++++++ | 41% ~05m 24s
|++++++++++++++++++++++ | 43% ~05m 16s
|+++++++++++++++++++++++ | 44% ~05m 09s
|+++++++++++++++++++++++ | 46% ~05m 02s
|++++++++++++++++++++++++ | 47% ~04m 55s
|+++++++++++++++++++++++++ | 49% ~04m 47s
|+++++++++++++++++++++++++ | 50% ~04m 39s
|++++++++++++++++++++++++++ | 51% ~04m 33s
|+++++++++++++++++++++++++++ | 53% ~04m 25s
|++++++++++++++++++++++++++++ | 54% ~04m 18s
|++++++++++++++++++++++++++++ | 56% ~04m 10s
|+++++++++++++++++++++++++++++ | 57% ~04m 02s
|++++++++++++++++++++++++++++++ | 59% ~03m 53s
|+++++++++++++++++++++++++++++++ | 60% ~03m 45s
|+++++++++++++++++++++++++++++++ | 62% ~03m 36s
|++++++++++++++++++++++++++++++++ | 63% ~03m 28s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 20s
|++++++++++++++++++++++++++++++++++ | 66% ~03m 11s
|++++++++++++++++++++++++++++++++++ | 68% ~03m 02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 54s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 45s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 37s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 19s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02m 11s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02m 03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 55s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 47s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 39s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 31s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 23s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~58s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~49s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~41s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~33s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~25s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 16s
[1] "a3_2"
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~08m 55s
|++ | 3 % ~08m 11s
|+++ | 4 % ~08m 03s
|+++ | 6 % ~08m 13s
|++++ | 7 % ~08m 12s
|+++++ | 9 % ~08m 01s
|++++++ | 10% ~07m 48s
|++++++ | 12% ~07m 40s
|+++++++ | 13% ~07m 29s
|++++++++ | 15% ~07m 21s
|+++++++++ | 16% ~07m 09s
|+++++++++ | 18% ~07m 06s
|++++++++++ | 19% ~07m 00s
|+++++++++++ | 21% ~06m 58s
|++++++++++++ | 22% ~06m 54s
|++++++++++++ | 24% ~06m 46s
|+++++++++++++ | 25% ~06m 37s
|++++++++++++++ | 26% ~06m 32s
|++++++++++++++ | 28% ~06m 24s
|+++++++++++++++ | 29% ~06m 15s
|++++++++++++++++ | 31% ~06m 06s
|+++++++++++++++++ | 32% ~05m 58s
|+++++++++++++++++ | 34% ~05m 50s
|++++++++++++++++++ | 35% ~05m 42s
|+++++++++++++++++++ | 37% ~05m 34s
|++++++++++++++++++++ | 38% ~05m 27s
|++++++++++++++++++++ | 40% ~05m 20s
|+++++++++++++++++++++ | 41% ~05m 14s
|++++++++++++++++++++++ | 43% ~05m 07s
|+++++++++++++++++++++++ | 44% ~05m 00s
|+++++++++++++++++++++++ | 46% ~04m 53s
|++++++++++++++++++++++++ | 47% ~04m 45s
|+++++++++++++++++++++++++ | 49% ~04m 37s
|+++++++++++++++++++++++++ | 50% ~04m 29s
|++++++++++++++++++++++++++ | 51% ~04m 22s
|+++++++++++++++++++++++++++ | 53% ~04m 14s
|++++++++++++++++++++++++++++ | 54% ~04m 06s
|++++++++++++++++++++++++++++ | 56% ~03m 58s
|+++++++++++++++++++++++++++++ | 57% ~03m 50s
|++++++++++++++++++++++++++++++ | 59% ~03m 42s
|+++++++++++++++++++++++++++++++ | 60% ~03m 34s
|+++++++++++++++++++++++++++++++ | 62% ~03m 26s
|++++++++++++++++++++++++++++++++ | 63% ~03m 18s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 10s
|++++++++++++++++++++++++++++++++++ | 66% ~03m 01s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 53s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 45s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 37s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 29s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 21s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 13s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02m 05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 57s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 50s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 42s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 34s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 26s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~55s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~47s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~39s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~31s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08m 50s
Nucleosome and TSS signal (serve as QC metrics)
# match regions with MT genes
allregions = rownames(srat_macs_l$a1_1)
ischrmt = lapply(chrmt, function(x) grepl(x, allregions))
ischrmt = Reduce(cbind, ischrmt)
ischrmt = rowSums(ischrmt)>0
for(n in names(srat_macs_l)){
all_cl = ClosestFeature(srat_macs_l[[n]], regions = allregions[ischrmt])
## get MT regions
mt_peaks = all_cl[all_cl$gene_id %in% mtid,"query_region"]
srat_macs_l[[n]] = PercentageFeatureSet(srat_macs_l[[n]], features = mt_peaks, col.name = "mt_perc")
}
Save data
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l_raw.RDS")
Plotting QC metrics
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l_raw.RDS")
Removing cells based on QC metrics
pdf("results/multiome/vln_qc_ATAC.pdf", width = 14, height = 4)
vln_atac_plts = list()
for(n in names(srat_atac_l)){
vln_atac_plts[[n]] = VlnPlot(object = srat_atac_l[[n]], pt.size = 0.1, ncol = 5, log = T,
features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
'TSS.enrichment', 'nucleosome_signal'))
print(vln_atac_plts[[n]])
}
dev.off()
null device
1
pdf("results/multiome/vln_qc_MACS.pdf", width = 14, height = 4)
vln_macs_plts = list()
for(n in names(srat_macs_l)){
vln_macs_plts[[n]] = VlnPlot(object = srat_macs_l[[n]], pt.size = 0.1, ncol = 5, log = T,
features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
'TSS.enrichment', 'nucleosome_signal'))
print(vln_macs_plts[[n]])
}
dev.off()
null device
1
Combine all ATAC data
# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2))
names(qc_list) = names(srat_atac_l)
for(n in names(srat_atac_l)){
srat_atac_l[[n]] = subset(x = srat_atac_l[[n]],
subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
mt_perc<qc_list[[n]][7])
}
srat_atac_l
$a1_1
An object of class Seurat
631645 features across 6187 samples within 1 assay
Active assay: ATAC (631645 features, 504027 variable features)
1 dimensional reduction calculated: lsi
$a1_2
An object of class Seurat
631645 features across 7135 samples within 1 assay
Active assay: ATAC (631645 features, 474058 variable features)
1 dimensional reduction calculated: lsi
$a3_1
An object of class Seurat
631645 features across 6003 samples within 1 assay
Active assay: ATAC (631645 features, 417276 variable features)
1 dimensional reduction calculated: lsi
$a3_2
An object of class Seurat
631645 features across 6324 samples within 1 assay
Active assay: ATAC (631645 features, 425918 variable features)
1 dimensional reduction calculated: lsi
# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5))
names(qc_list) = names(srat_macs_l)
for(n in names(srat_macs_l)){
srat_macs_l[[n]] = subset(x = srat_macs_l[[n]],
subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
mt_perc<qc_list[[n]][7])
}
srat_macs_l
$a1_1
An object of class Seurat
135606 features across 6408 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a1_2
An object of class Seurat
135606 features across 7308 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a3_1
An object of class Seurat
135606 features across 6011 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a3_2
An object of class Seurat
135606 features across 6372 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
Check how the data looks between samples
# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_atac_l)){
cn = unlist(lapply(strsplit(colnames(srat_atac_l[[n]]), "-", fixed = T), function(x) x[1]))
srat_atac_l[[n]] = RenameCells(srat_atac_l[[n]],
new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}
# merging
srat_atac_all = Reduce(merge, srat_atac_l)
# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_macs_l)){
cn = unlist(lapply(strsplit(colnames(srat_macs_l[[n]]), "-", fixed = T), function(x) x[1]))
srat_macs_l[[n]] = RenameCells(srat_macs_l[[n]],
new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}
# merging
srat_macs_all = Reduce(merge, srat_macs_l)
Processing ATAC-seq data
CoveragePlot(object = srat_atac_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
CoveragePlot(object = srat_macs_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
Check correlation of LSI with sequencing depth (since often times the first component is technical var)
srat_atac_all = RunTFIDF(srat_atac_all)
Performing TF-IDF normalization
Some features contain 0 total counts
srat_atac_all = FindTopFeatures(srat_atac_all, min.cutoff = 5)
srat_atac_all = RunSVD(srat_atac_all)
Running SVD
Scaling cell embeddings
srat_macs_all = RunTFIDF(srat_macs_all)
Performing TF-IDF normalization
Some features contain 0 total counts
srat_macs_all = FindTopFeatures(srat_macs_all, min.cutoff = 5)
srat_macs_all = RunSVD(srat_macs_all)
Running SVD
Scaling cell embeddings
Get UMAP
DepthCor(srat_atac_all, n = 30)
DepthCor(srat_macs_all, n = 30)
Processing for each ATAC-seq sample
cordat = DepthCor(srat_atac_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_atac_all = RunUMAP(object = srat_atac_all, reduction = 'lsi', dims = dims_use, verbose = F)
The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
DimPlot(object = srat_atac_all, group.by = "dataset")
FeaturePlot(object = srat_atac_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
cordat = DepthCor(srat_macs_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_macs_all = RunUMAP(object = srat_macs_all, reduction = 'lsi', dims = dims_use, verbose = F)
DimPlot(object = srat_macs_all, group.by = "dataset")
FeaturePlot(object = srat_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Integrate based on each experiment - CellRanger peaks
for(n in names(srat_atac_l)){
srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = "q0")
srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
}
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
DepthCor(srat_atac_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_2, reduction = "lsi", n = 30)
for(n in names(srat_macs_l)){
srat_macs_l[[n]] = RunTFIDF(srat_macs_l[[n]])
srat_macs_l[[n]] = FindTopFeatures(srat_macs_l[[n]], min.cutoff = "q0")
srat_macs_l[[n]] = RunSVD(srat_macs_l[[n]])
}
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
DepthCor(srat_macs_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_2, reduction = "lsi", n = 30)
Integrate based on each experiment - MACS3 peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_l, reduction = "rlsi", dims = 2:30,
anchor.features = rownames(srat_atac_all))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++ | 25% ~06s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Finding all pairwise anchors
| | 0 % ~calculating
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1383 anchors
|+++++++++ | 17% ~03m 49s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 516 anchors
|+++++++++++++++++ | 33% ~02m 51s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 460 anchors
|+++++++++++++++++++++++++ | 50% ~02m 05s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 639 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~01m 27s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 536 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~44s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 655 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 20s
# integrate LSI embeddings
integ_all = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)
Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 3 into 2 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(integ_all, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_all = RunUMAP(integ_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_all, group.by = "dataset")
FeaturePlot(object = integ_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Processing ATAC-seq by animal
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_l, reduction = "rlsi", dims = 2:30,
anchor.features = rownames(srat_macs_all))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++ | 25% ~06s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Finding all pairwise anchors
| | 0 % ~calculating
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1011 anchors
|+++++++++ | 17% ~01m 48s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 436 anchors
|+++++++++++++++++ | 33% ~01m 26s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 359 anchors
|+++++++++++++++++++++++++ | 50% ~01m 01s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 550 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~42s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 436 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~21s
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 475 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 04s
# integrate LSI embeddings
integ_macs_all = IntegrateEmbeddings(anchorset = integration.anchors,
reductions = srat_macs_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)
Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 3 into 2 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_all = RunUMAP(integ_macs_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_all, group.by = "dataset")
FeaturePlot(object = integ_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Integrate based on each animal - CellRanger peaks
srat_atac_animal = SplitObject(srat_atac_all, split.by = "animal")
for(n in names(srat_atac_animal)){
srat_atac_animal[[n]] = RunTFIDF(srat_atac_animal[[n]])
srat_atac_animal[[n]] = FindTopFeatures(srat_atac_animal[[n]], min.cutoff = "q0")
srat_atac_animal[[n]] = RunSVD(srat_atac_animal[[n]])
}
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
DepthCor(srat_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_atac_animal$a3, reduction = "lsi", n = 30)
srat_macs_animal = SplitObject(srat_macs_all, split.by = "animal")
for(n in names(srat_macs_animal)){
srat_macs_animal[[n]] = RunTFIDF(srat_macs_animal[[n]])
srat_macs_animal[[n]] = FindTopFeatures(srat_macs_animal[[n]], min.cutoff = "q0")
srat_macs_animal[[n]] = RunSVD(srat_macs_animal[[n]])
}
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
DepthCor(srat_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_macs_animal$a3, reduction = "lsi", n = 30)
Integrate based on each animal - MACS3 peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(srat_atac_all))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s
Finding all pairwise anchors
| | 0 % ~calculating
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1294 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 44s
# integrate LSI embeddings
integ_animal = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)
Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_animal = RunUMAP(integ_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_animal, group.by = "dataset")
FeaturePlot(object = integ_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Saving data so far
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(srat_macs_all))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Finding all pairwise anchors
| | 0 % ~calculating
No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1101 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=48s
# integrate LSI embeddings
integ_macs_animal = IntegrateEmbeddings(anchorset = integration.anchors,
reductions = srat_macs_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)
Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = RunUMAP(integ_macs_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_animal, group.by = "dataset")
FeaturePlot(object = integ_macs_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
We’re going with the integration by animal Find clusters
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l.RDS")
saveRDS(srat_atac_animal, file = "data/processed/multiome/srat_atac_animal.RDS")
saveRDS(srat_atac_all, file = "data/processed/multiome/srat_atac_all.RDS")
saveRDS(integ_all, file = "data/processed/multiome/integ_all.RDS")
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal.RDS")
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l.RDS")
saveRDS(srat_macs_animal, file = "data/processed/multiome/srat_macs_animal.RDS")
saveRDS(srat_macs_all, file = "data/processed/multiome/srat_macs_all.RDS")
saveRDS(integ_macs_all, file = "data/processed/multiome/integ_macs_all.RDS")
Get DE peaks
DimPlot(object = integ_animal, label = TRUE) + NoLegend()
cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = FindNeighbors(object = integ_macs_animal, reduction = 'integrated_lsi',
dims = dims_use,
force.recalc = T, graph.name = "thegraph")
Computing nearest neighbor graph
Computing SNN
Only one graph name supplied, storing nearest-neighbor graph only
integ_macs_animal = FindClusters(object = integ_macs_animal, verbose = FALSE, algorithm = 3,
graph.name = "thegraph", resolution = 0.8)
DimPlot(object = integ_macs_animal, label = TRUE) + NoLegend()
Annotate said peaks
reg = unique(allmk_ATAC$gene)
closest_allmk = ClosestFeature(integ_animal, regions = unique(reg))
allmk_ATAC = merge(allmk_ATAC, closest_allmk[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_ATAC, file = "results/multiome/allmk_ATAC.csv", col.names = T, row.names = F, quote = F)
reg = unique(allmk_macs$gene)
closest_allmk_macs = ClosestFeature(integ_macs_animal, regions = unique(reg))
allmk_macs = merge(allmk_macs[,-c(8:10)],
closest_allmk_macs[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_macs, file = "results/multiome/allmk_macs.csv", col.names = T, row.names = F, quote = F)
Example coverage plot
reg = unique(allmk_ATAC$gene)
closest_allmk = ClosestFeature(integ_animal, regions = unique(reg))
allmk_ATAC = merge(allmk_ATAC, closest_allmk[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_ATAC, file = "results/multiome/allmk_ATAC.csv", col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
reg = unique(allmk_macs$gene)
closest_allmk_macs = ClosestFeature(integ_macs_animal, regions = unique(reg))
allmk_macs = merge(allmk_macs[,-c(8:10)],
closest_allmk_macs[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_macs, file = "results/multiome/allmk_macs.csv", col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
Obtaining gene activities - helps give meaning to the detected peaks
CoveragePlot(object = integ_animal, region = "chr8ps1-430732645-430746183",
extend.upstream = 5000, extend.downstream = 5000)
CoveragePlot(object = integ_macs_animal, region = "chr8ps1-430732645-430746183",
extend.upstream = 5000, extend.downstream = 5000)
Plotting gene activities and get DE genes
gene_act = GeneActivity(integ_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)
Extracting gene coordinates
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~12m 32s
|++++ | 7 % ~10m 26s
|++++++ | 10% ~09m 44s
|+++++++ | 14% ~09m 09s
|+++++++++ | 17% ~08m 51s
|+++++++++++ | 21% ~08m 25s
|+++++++++++++ | 24% ~08m 03s
|++++++++++++++ | 28% ~07m 32s
|++++++++++++++++ | 31% ~07m 06s
|++++++++++++++++++ | 34% ~06m 44s
|+++++++++++++++++++ | 38% ~06m 21s
|+++++++++++++++++++++ | 41% ~05m 59s
|+++++++++++++++++++++++ | 45% ~05m 39s
|+++++++++++++++++++++++++ | 48% ~05m 17s
|++++++++++++++++++++++++++ | 52% ~04m 54s
|++++++++++++++++++++++++++++ | 55% ~04m 34s
|++++++++++++++++++++++++++++++ | 59% ~04m 13s
|++++++++++++++++++++++++++++++++ | 62% ~03m 51s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 31s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 48s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 30s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 49s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 10s
|++++ | 7 % ~09m 12s
|++++++ | 10% ~09m 35s
|+++++++ | 14% ~09m 48s
|+++++++++ | 17% ~09m 30s
|+++++++++++ | 21% ~09m 13s
|+++++++++++++ | 24% ~08m 55s
|++++++++++++++ | 28% ~08m 24s
|++++++++++++++++ | 31% ~07m 58s
|++++++++++++++++++ | 34% ~07m 39s
|+++++++++++++++++++ | 38% ~07m 07s
|+++++++++++++++++++++ | 41% ~06m 39s
|+++++++++++++++++++++++ | 45% ~06m 13s
|+++++++++++++++++++++++++ | 48% ~05m 47s
|++++++++++++++++++++++++++ | 52% ~05m 19s
|++++++++++++++++++++++++++++ | 55% ~04m 55s
|++++++++++++++++++++++++++++++ | 59% ~04m 31s
|++++++++++++++++++++++++++++++++ | 62% ~04m 06s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 44s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 20s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 58s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 35s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 50s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 33s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 14s
|++++ | 7 % ~08m 57s
|++++++ | 10% ~08m 37s
|+++++++ | 14% ~08m 33s
|+++++++++ | 17% ~08m 18s
|+++++++++++ | 21% ~08m 05s
|+++++++++++++ | 24% ~07m 46s
|++++++++++++++ | 28% ~07m 19s
|++++++++++++++++ | 31% ~06m 58s
|++++++++++++++++++ | 34% ~06m 36s
|+++++++++++++++++++ | 38% ~06m 15s
|+++++++++++++++++++++ | 41% ~05m 56s
|+++++++++++++++++++++++ | 45% ~05m 35s
|+++++++++++++++++++++++++ | 48% ~05m 14s
|++++++++++++++++++++++++++ | 52% ~04m 53s
|++++++++++++++++++++++++++++ | 55% ~04m 33s
|++++++++++++++++++++++++++++++ | 59% ~04m 12s
|++++++++++++++++++++++++++++++++ | 62% ~03m 51s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 31s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 49s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 28s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 46s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 26s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 45s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~12m 36s
|++++ | 7 % ~11m 33s
|++++++ | 10% ~11m 13s
|+++++++ | 14% ~10m 44s
|+++++++++ | 17% ~10m 28s
|+++++++++++ | 21% ~10m 01s
|+++++++++++++ | 24% ~09m 40s
|++++++++++++++ | 28% ~09m 03s
|++++++++++++++++ | 31% ~08m 31s
|++++++++++++++++++ | 34% ~08m 07s
|+++++++++++++++++++ | 38% ~07m 33s
|+++++++++++++++++++++ | 41% ~07m 04s
|+++++++++++++++++++++++ | 45% ~06m 35s
|+++++++++++++++++++++++++ | 48% ~06m 05s
|++++++++++++++++++++++++++ | 52% ~05m 37s
|++++++++++++++++++++++++++++ | 55% ~05m 12s
|++++++++++++++++++++++++++++++ | 59% ~04m 46s
|++++++++++++++++++++++++++++++++ | 62% ~04m 22s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 58s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 34s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03m 10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 45s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 22s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 58s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 34s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~47s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~23s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11m 18s
# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)
# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_animal = NormalizeData(object = integ_animal, assay = 'GeneAct',
normalization.method = 'LogNormalize',
scale.factor = median(integ_animal$nCount_GeneAct))
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
gene_act = GeneActivity(integ_macs_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)
Extracting gene coordinates
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~13m 50s
|++++ | 7 % ~11m 07s
|++++++ | 10% ~10m 26s
|+++++++ | 14% ~09m 45s
|+++++++++ | 17% ~09m 11s
|+++++++++++ | 21% ~08m 48s
|+++++++++++++ | 24% ~08m 21s
|++++++++++++++ | 28% ~07m 48s
|++++++++++++++++ | 31% ~07m 24s
|++++++++++++++++++ | 34% ~06m 59s
|+++++++++++++++++++ | 38% ~06m 36s
|+++++++++++++++++++++ | 41% ~06m 14s
|+++++++++++++++++++++++ | 45% ~05m 52s
|+++++++++++++++++++++++++ | 48% ~05m 30s
|++++++++++++++++++++++++++ | 52% ~05m 07s
|++++++++++++++++++++++++++++ | 55% ~04m 45s
|++++++++++++++++++++++++++++++ | 59% ~04m 23s
|++++++++++++++++++++++++++++++++ | 62% ~04m 00s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 39s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 16s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 55s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 27s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 29s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~09m 54s
|++++ | 7 % ~08m 49s
|++++++ | 10% ~08m 34s
|+++++++ | 14% ~08m 19s
|+++++++++ | 17% ~08m 02s
|+++++++++++ | 21% ~07m 44s
|+++++++++++++ | 24% ~07m 26s
|++++++++++++++ | 28% ~07m 01s
|++++++++++++++++ | 31% ~06m 39s
|++++++++++++++++++ | 34% ~06m 18s
|+++++++++++++++++++ | 38% ~05m 58s
|+++++++++++++++++++++ | 41% ~05m 39s
|+++++++++++++++++++++++ | 45% ~05m 20s
|+++++++++++++++++++++++++ | 48% ~05m 01s
|++++++++++++++++++++++++++ | 52% ~04m 39s
|++++++++++++++++++++++++++++ | 55% ~04m 21s
|++++++++++++++++++++++++++++++ | 59% ~04m 01s
|++++++++++++++++++++++++++++++++ | 62% ~03m 40s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 21s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 42s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 42s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 21s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~41s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 52s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 03s
|++++ | 7 % ~08m 57s
|++++++ | 10% ~08m 37s
|+++++++ | 14% ~08m 31s
|+++++++++ | 17% ~08m 17s
|+++++++++++ | 21% ~08m 00s
|+++++++++++++ | 24% ~07m 44s
|++++++++++++++ | 28% ~07m 18s
|++++++++++++++++ | 31% ~06m 58s
|++++++++++++++++++ | 34% ~06m 42s
|+++++++++++++++++++ | 38% ~06m 23s
|+++++++++++++++++++++ | 41% ~06m 05s
|+++++++++++++++++++++++ | 45% ~05m 45s
|+++++++++++++++++++++++++ | 48% ~05m 24s
|++++++++++++++++++++++++++ | 52% ~05m 02s
|++++++++++++++++++++++++++++ | 55% ~04m 42s
|++++++++++++++++++++++++++++++ | 59% ~04m 21s
|++++++++++++++++++++++++++++++++ | 62% ~03m 59s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 38s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 33s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 50s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 37s
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~09m 32s
|++++ | 7 % ~08m 54s
|++++++ | 10% ~08m 52s
|+++++++ | 14% ~09m 04s
|+++++++++ | 17% ~08m 52s
|+++++++++++ | 21% ~08m 35s
|+++++++++++++ | 24% ~08m 09s
|++++++++++++++ | 28% ~07m 39s
|++++++++++++++++ | 31% ~07m 15s
|++++++++++++++++++ | 34% ~06m 51s
|+++++++++++++++++++ | 38% ~06m 28s
|+++++++++++++++++++++ | 41% ~06m 07s
|+++++++++++++++++++++++ | 45% ~05m 46s
|+++++++++++++++++++++++++ | 48% ~05m 24s
|++++++++++++++++++++++++++ | 52% ~05m 02s
|++++++++++++++++++++++++++++ | 55% ~04m 42s
|++++++++++++++++++++++++++++++ | 59% ~04m 20s
|++++++++++++++++++++++++++++++++ | 62% ~03m 59s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 38s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 34s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 50s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 41s
# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)
# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_macs_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_macs_animal = NormalizeData(object = integ_macs_animal, assay = 'GeneAct',
normalization.method = 'LogNormalize',
scale.factor = median(integ_macs_animal$nCount_GeneAct))
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Save object with annotations and gene activities
DefaultAssay(integ_animal) = 'GeneAct'
FeaturePlot(object = integ_animal, features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)
allmk_geneact = FindAllMarkers(object = integ_animal, min.pct = 0.2,
test.use = 'LR', latent.vars = 'nCount_GeneAct')
Calculating cluster 0
| | 0 % ~calculating
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+ | 2 % ~10s
|++ | 3 % ~09s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|+++++ | 8 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 13% ~09s
|++++++++ | 15% ~09s
|+++++++++ | 16% ~09s
|+++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 21% ~09s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~08s
|+++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|++++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~39s
|++ | 2 % ~37s
|++ | 3 % ~37s
|+++ | 5 % ~36s
|+++ | 6 % ~35s
|++++ | 7 % ~35s
|+++++ | 8 % ~34s
|+++++ | 9 % ~34s
|++++++ | 10% ~34s
|++++++ | 12% ~33s
|+++++++ | 13% ~32s
|+++++++ | 14% ~32s
|++++++++ | 15% ~32s
|+++++++++ | 16% ~31s
|+++++++++ | 17% ~31s
|++++++++++ | 19% ~31s
|++++++++++ | 20% ~30s
|+++++++++++ | 21% ~30s
|++++++++++++ | 22% ~30s
|++++++++++++ | 23% ~29s
|+++++++++++++ | 24% ~29s
|+++++++++++++ | 26% ~29s
|++++++++++++++ | 27% ~28s
|++++++++++++++ | 28% ~28s
|+++++++++++++++ | 29% ~28s
|++++++++++++++++ | 30% ~27s
|++++++++++++++++ | 31% ~27s
|+++++++++++++++++ | 33% ~27s
|+++++++++++++++++ | 34% ~26s
|++++++++++++++++++ | 35% ~50s
|+++++++++++++++++++ | 36% ~49s
|+++++++++++++++++++ | 37% ~47s
|++++++++++++++++++++ | 38% ~46s
|++++++++++++++++++++ | 40% ~44s
|+++++++++++++++++++++ | 41% ~43s
|+++++++++++++++++++++ | 42% ~42s
|++++++++++++++++++++++ | 43% ~40s
|+++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~38s
|++++++++++++++++++++++++ | 47% ~37s
|++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|+++++++++++++++++++++++++ | 50% ~33s
|++++++++++++++++++++++++++ | 51% ~32s
|+++++++++++++++++++++++++++ | 52% ~31s
|+++++++++++++++++++++++++++ | 53% ~30s
|++++++++++++++++++++++++++++ | 55% ~29s
|++++++++++++++++++++++++++++ | 56% ~28s
|+++++++++++++++++++++++++++++ | 57% ~27s
|++++++++++++++++++++++++++++++ | 58% ~26s
|++++++++++++++++++++++++++++++ | 59% ~25s
|+++++++++++++++++++++++++++++++ | 60% ~24s
|+++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|++++++++++++++++++++++++++++++++ | 64% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|++++++++++++++++++++++++++++++++++ | 66% ~20s
|++++++++++++++++++++++++++++++++++ | 67% ~19s
|+++++++++++++++++++++++++++++++++++ | 69% ~18s
|+++++++++++++++++++++++++++++++++++ | 70% ~18s
|++++++++++++++++++++++++++++++++++++ | 71% ~17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~16s
|+++++++++++++++++++++++++++++++++++++ | 73% ~15s
|++++++++++++++++++++++++++++++++++++++ | 74% ~15s
|++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=53s
Calculating cluster 3
| | 0 % ~calculating
|+ | 2 % ~17s
|++ | 4 % ~18s
|+++ | 6 % ~17s
|++++ | 8 % ~16s
|+++++ | 9 % ~15s
|++++++ | 11% ~15s
|+++++++ | 13% ~15s
|++++++++ | 15% ~15s
|+++++++++ | 17% ~15s
|++++++++++ | 19% ~14s
|+++++++++++ | 21% ~14s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 25% ~13s
|++++++++++++++ | 26% ~13s
|+++++++++++++++ | 28% ~12s
|++++++++++++++++ | 30% ~12s
|+++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|++++++++++++++++++++++++++++++ | 58% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s
Calculating cluster 4
| | 0 % ~calculating
|+ | 2 % ~30s
|++ | 3 % ~28s
|+++ | 5 % ~27s
|++++ | 7 % ~26s
|+++++ | 8 % ~26s
|+++++ | 10% ~25s
|++++++ | 11% ~24s
|+++++++ | 13% ~24s
|++++++++ | 15% ~24s
|+++++++++ | 16% ~41s
|++++++++++ | 18% ~38s
|++++++++++ | 20% ~35s
|+++++++++++ | 21% ~33s
|++++++++++++ | 23% ~31s
|+++++++++++++ | 25% ~30s
|++++++++++++++ | 26% ~28s
|++++++++++++++ | 28% ~27s
|+++++++++++++++ | 30% ~26s
|++++++++++++++++ | 31% ~25s
|+++++++++++++++++ | 33% ~23s
|++++++++++++++++++ | 34% ~22s
|+++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 48% ~16s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|+++++++++++++++++++++++++++ | 52% ~14s
|++++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 59% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|++++++++++++++++++++++++++++++++ | 62% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~01m 11s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 06s
|+++ | 4 % ~01m 05s
|+++ | 5 % ~01m 04s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 03s
|+++++ | 8 % ~01m 02s
|+++++ | 9 % ~01m 01s
|++++++ | 11% ~01m 00s
|++++++ | 12% ~59s
|+++++++ | 13% ~59s
|+++++++ | 14% ~58s
|++++++++ | 15% ~58s
|++++++++ | 16% ~57s
|+++++++++ | 17% ~57s
|+++++++++ | 18% ~56s
|++++++++++ | 19% ~55s
|++++++++++ | 20% ~54s
|+++++++++++ | 21% ~54s
|++++++++++++ | 22% ~53s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~51s
|++++++++++++++ | 26% ~50s
|++++++++++++++ | 27% ~50s
|+++++++++++++++ | 28% ~49s
|+++++++++++++++ | 29% ~48s
|++++++++++++++++ | 31% ~47s
|++++++++++++++++ | 32% ~47s
|+++++++++++++++++ | 33% ~46s
|+++++++++++++++++ | 34% ~45s
|++++++++++++++++++ | 35% ~44s
|++++++++++++++++++ | 36% ~44s
|+++++++++++++++++++ | 37% ~43s
|+++++++++++++++++++ | 38% ~42s
|++++++++++++++++++++ | 39% ~42s
|++++++++++++++++++++ | 40% ~41s
|+++++++++++++++++++++ | 41% ~40s
|++++++++++++++++++++++ | 42% ~39s
|++++++++++++++++++++++ | 43% ~39s
|+++++++++++++++++++++++ | 44% ~38s
|+++++++++++++++++++++++ | 45% ~37s
|++++++++++++++++++++++++ | 46% ~37s
|++++++++++++++++++++++++ | 47% ~36s
|+++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~35s
|++++++++++++++++++++++++++ | 51% ~35s
|++++++++++++++++++++++++++ | 52% ~34s
|+++++++++++++++++++++++++++ | 53% ~34s
|+++++++++++++++++++++++++++ | 54% ~33s
|++++++++++++++++++++++++++++ | 55% ~32s
|++++++++++++++++++++++++++++ | 56% ~32s
|+++++++++++++++++++++++++++++ | 57% ~33s
|+++++++++++++++++++++++++++++ | 58% ~32s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|++++++++++++++++++++++++++++++++ | 62% ~29s
|++++++++++++++++++++++++++++++++ | 63% ~28s
|+++++++++++++++++++++++++++++++++ | 64% ~27s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|++++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|+++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~22s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14s
Calculating cluster 6
| | 0 % ~calculating
|+ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 18% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s
Calculating cluster 7
| | 0 % ~calculating
|+ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 8 % ~10s
|+++++ | 9 % ~11s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|++++++++ | 14% ~09s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 28% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|++ | 2 % ~01m 11s
|++ | 3 % ~01m 10s
|+++ | 5 % ~01m 09s
|+++ | 6 % ~01m 08s
|++++ | 7 % ~01m 07s
|++++ | 8 % ~01m 06s
|+++++ | 9 % ~01m 06s
|++++++ | 10% ~01m 05s
|++++++ | 11% ~01m 04s
|+++++++ | 12% ~01m 03s
|+++++++ | 14% ~01m 02s
|++++++++ | 15% ~01m 01s
|++++++++ | 16% ~01m 00s
|+++++++++ | 17% ~60s
|++++++++++ | 18% ~59s
|++++++++++ | 19% ~58s
|+++++++++++ | 20% ~57s
|+++++++++++ | 22% ~56s
|++++++++++++ | 23% ~55s
|++++++++++++ | 24% ~54s
|+++++++++++++ | 25% ~53s
|++++++++++++++ | 26% ~52s
|++++++++++++++ | 27% ~52s
|+++++++++++++++ | 28% ~51s
|+++++++++++++++ | 30% ~50s
|++++++++++++++++ | 31% ~49s
|++++++++++++++++ | 32% ~48s
|+++++++++++++++++ | 33% ~48s
|++++++++++++++++++ | 34% ~47s
|++++++++++++++++++ | 35% ~46s
|+++++++++++++++++++ | 36% ~45s
|+++++++++++++++++++ | 38% ~44s
|++++++++++++++++++++ | 39% ~43s
|++++++++++++++++++++ | 40% ~43s
|+++++++++++++++++++++ | 41% ~42s
|++++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~40s
|+++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~39s
|++++++++++++++++++++++++ | 47% ~38s
|++++++++++++++++++++++++ | 48% ~37s
|+++++++++++++++++++++++++ | 49% ~36s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~35s
|+++++++++++++++++++++++++++ | 52% ~34s
|+++++++++++++++++++++++++++ | 53% ~33s
|++++++++++++++++++++++++++++ | 55% ~32s
|++++++++++++++++++++++++++++ | 56% ~31s
|+++++++++++++++++++++++++++++ | 57% ~31s
|+++++++++++++++++++++++++++++ | 58% ~30s
|++++++++++++++++++++++++++++++ | 59% ~29s
|+++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|++++++++++++++++++++++++++++++++ | 62% ~27s
|++++++++++++++++++++++++++++++++ | 64% ~26s
|+++++++++++++++++++++++++++++++++ | 65% ~25s
|+++++++++++++++++++++++++++++++++ | 66% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~24s
|+++++++++++++++++++++++++++++++++++ | 68% ~23s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|++++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 72% ~20s
|+++++++++++++++++++++++++++++++++++++ | 73% ~20s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14s
Calculating cluster 9
| | 0 % ~calculating
|++ | 3 % ~06s
|++++ | 6 % ~06s
|+++++ | 9 % ~06s
|+++++++ | 12% ~06s
|++++++++ | 15% ~05s
|++++++++++ | 18% ~05s
|+++++++++++ | 21% ~05s
|+++++++++++++ | 24% ~05s
|++++++++++++++ | 27% ~05s
|++++++++++++++++ | 30% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++++ | 36% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++++ | 42% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++++ | 48% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s
Calculating cluster 10
| | 0 % ~calculating
|++ | 2 % ~08s
|+++ | 4 % ~08s
|++++ | 7 % ~08s
|+++++ | 9 % ~08s
|++++++ | 11% ~08s
|+++++++ | 13% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 24% ~07s
|++++++++++++++ | 26% ~06s
|+++++++++++++++ | 28% ~06s
|++++++++++++++++ | 30% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 50% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
Calculating cluster 11
| | 0 % ~calculating
|+ | 2 % ~26s
|++ | 3 % ~24s
|+++ | 5 % ~24s
|++++ | 6 % ~24s
|++++ | 8 % ~24s
|+++++ | 9 % ~23s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 14% ~22s
|++++++++ | 15% ~22s
|+++++++++ | 17% ~21s
|++++++++++ | 18% ~21s
|++++++++++ | 20% ~20s
|+++++++++++ | 22% ~20s
|++++++++++++ | 23% ~19s
|+++++++++++++ | 25% ~19s
|++++++++++++++ | 26% ~19s
|++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~25s
|++++++++++++++++ | 31% ~24s
|+++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 34% ~23s
|++++++++++++++++++ | 35% ~22s
|+++++++++++++++++++ | 37% ~21s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~16s
|++++++++++++++++++++++++++ | 51% ~15s
|+++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~14s
|+++++++++++++++++++++++++++++ | 57% ~13s
|++++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|++++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=28s
Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 7 % ~14s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 20% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|+++++++++++++++ | 28% ~11s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 41% ~09s
|+++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s
Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~01m 21s
|++ | 2 % ~01m 26s
|++ | 4 % ~01m 28s
|+++ | 5 % ~01m 27s
|++++ | 6 % ~01m 25s
|++++ | 7 % ~01m 24s
|+++++ | 9 % ~01m 21s
|+++++ | 10% ~01m 20s
|++++++ | 11% ~01m 19s
|+++++++ | 12% ~01m 18s
|+++++++ | 14% ~01m 17s
|++++++++ | 15% ~01m 16s
|+++++++++ | 16% ~01m 31s
|+++++++++ | 17% ~01m 28s
|++++++++++ | 19% ~01m 26s
|++++++++++ | 20% ~01m 24s
|+++++++++++ | 21% ~01m 22s
|++++++++++++ | 22% ~01m 20s
|++++++++++++ | 23% ~01m 18s
|+++++++++++++ | 25% ~01m 16s
|+++++++++++++ | 26% ~01m 15s
|++++++++++++++ | 27% ~01m 13s
|+++++++++++++++ | 28% ~01m 12s
|+++++++++++++++ | 30% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|+++++++++++++++++ | 32% ~01m 07s
|+++++++++++++++++ | 33% ~01m 06s
|++++++++++++++++++ | 35% ~01m 04s
|++++++++++++++++++ | 36% ~01m 03s
|+++++++++++++++++++ | 37% ~01m 01s
|++++++++++++++++++++ | 38% ~60s
|++++++++++++++++++++ | 40% ~58s
|+++++++++++++++++++++ | 41% ~57s
|+++++++++++++++++++++ | 42% ~56s
|++++++++++++++++++++++ | 43% ~54s
|+++++++++++++++++++++++ | 44% ~53s
|+++++++++++++++++++++++ | 46% ~52s
|++++++++++++++++++++++++ | 47% ~50s
|+++++++++++++++++++++++++ | 48% ~49s
|+++++++++++++++++++++++++ | 49% ~48s
|++++++++++++++++++++++++++ | 51% ~47s
|++++++++++++++++++++++++++ | 52% ~45s
|+++++++++++++++++++++++++++ | 53% ~44s
|++++++++++++++++++++++++++++ | 54% ~43s
|++++++++++++++++++++++++++++ | 56% ~41s
|+++++++++++++++++++++++++++++ | 57% ~40s
|++++++++++++++++++++++++++++++ | 58% ~39s
|++++++++++++++++++++++++++++++ | 59% ~38s
|+++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 62% ~35s
|++++++++++++++++++++++++++++++++ | 63% ~34s
|+++++++++++++++++++++++++++++++++ | 64% ~33s
|+++++++++++++++++++++++++++++++++ | 65% ~32s
|++++++++++++++++++++++++++++++++++ | 67% ~31s
|++++++++++++++++++++++++++++++++++ | 68% ~30s
|+++++++++++++++++++++++++++++++++++ | 69% ~28s
|++++++++++++++++++++++++++++++++++++ | 70% ~27s
|++++++++++++++++++++++++++++++++++++ | 72% ~26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~25s
|++++++++++++++++++++++++++++++++++++++ | 74% ~24s
|++++++++++++++++++++++++++++++++++++++ | 75% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 34s
Calculating cluster 14
| | 0 % ~calculating
|+ | 1 % ~06m 51s
|+ | 2 % ~06m 40s
|++ | 3 % ~06m 37s
|++ | 4 % ~06m 34s
|+++ | 5 % ~06m 31s
|+++ | 6 % ~06m 27s
|++++ | 7 % ~06m 24s
|++++ | 8 % ~06m 21s
|+++++ | 9 % ~06m 17s
|+++++ | 10% ~06m 14s
|++++++ | 11% ~06m 13s
|++++++ | 12% ~06m 30s
|+++++++ | 13% ~06m 24s
|+++++++ | 14% ~06m 19s
|++++++++ | 15% ~06m 13s
|++++++++ | 16% ~06m 07s
|+++++++++ | 17% ~06m 02s
|+++++++++ | 18% ~05m 56s
|++++++++++ | 19% ~05m 51s
|++++++++++ | 20% ~05m 46s
|+++++++++++ | 21% ~05m 41s
|+++++++++++ | 22% ~05m 36s
|++++++++++++ | 23% ~05m 32s
|++++++++++++ | 24% ~05m 27s
|+++++++++++++ | 25% ~05m 23s
|+++++++++++++ | 26% ~05m 18s
|++++++++++++++ | 27% ~05m 15s
|++++++++++++++ | 28% ~05m 11s
|+++++++++++++++ | 29% ~05m 13s
|+++++++++++++++ | 30% ~05m 09s
|++++++++++++++++ | 31% ~05m 04s
|++++++++++++++++ | 32% ~04m 59s
|+++++++++++++++++ | 33% ~04m 54s
|+++++++++++++++++ | 34% ~04m 49s
|++++++++++++++++++ | 35% ~04m 44s
|++++++++++++++++++ | 36% ~04m 40s
|+++++++++++++++++++ | 37% ~04m 35s
|+++++++++++++++++++ | 38% ~04m 30s
|++++++++++++++++++++ | 39% ~04m 25s
|++++++++++++++++++++ | 40% ~04m 21s
|+++++++++++++++++++++ | 41% ~04m 17s
|+++++++++++++++++++++ | 42% ~04m 12s
|++++++++++++++++++++++ | 43% ~04m 08s
|++++++++++++++++++++++ | 44% ~04m 04s
|+++++++++++++++++++++++ | 45% ~03m 60s
|+++++++++++++++++++++++ | 46% ~03m 59s
|++++++++++++++++++++++++ | 47% ~03m 54s
|++++++++++++++++++++++++ | 48% ~03m 50s
|+++++++++++++++++++++++++ | 49% ~03m 45s
|+++++++++++++++++++++++++ | 50% ~03m 40s
|++++++++++++++++++++++++++ | 51% ~03m 36s
|++++++++++++++++++++++++++ | 52% ~03m 31s
|+++++++++++++++++++++++++++ | 53% ~03m 26s
|+++++++++++++++++++++++++++ | 54% ~03m 22s
|++++++++++++++++++++++++++++ | 55% ~03m 17s
|++++++++++++++++++++++++++++ | 56% ~03m 13s
|+++++++++++++++++++++++++++++ | 57% ~03m 08s
|+++++++++++++++++++++++++++++ | 58% ~03m 04s
|++++++++++++++++++++++++++++++ | 59% ~02m 59s
|++++++++++++++++++++++++++++++ | 60% ~02m 55s
|+++++++++++++++++++++++++++++++ | 61% ~02m 51s
|+++++++++++++++++++++++++++++++ | 62% ~02m 47s
|++++++++++++++++++++++++++++++++ | 63% ~02m 44s
|++++++++++++++++++++++++++++++++ | 64% ~02m 39s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 35s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 30s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 26s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 21s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 17s
|+++++++++++++++++++++++++++++++++++ | 70% ~02m 12s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 08s
|++++++++++++++++++++++++++++++++++++ | 72% ~02m 03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 59s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 54s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 50s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 46s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 41s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 32s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 24s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~57s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~53s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~48s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~44s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~40s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~35s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~31s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07m 20s
Calculating cluster 15
Calculating cluster 16
The following tests were not performed: When testing 15 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 16 versus all:
Cell group 1 has fewer than 3 cells
allmk_geneact = allmk_geneact[allmk_geneact$p_val_adj<=0.05,]
write.csv(allmk_geneact, file = "results/multiome/allmk_geneact.csv",
col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
DefaultAssay(integ_macs_animal) = 'GeneAct'
FeaturePlot(object = integ_macs_animal,
features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)
allmk_geneact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2,
test.use = 'LR', latent.vars = 'nCount_GeneAct')
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~12s
|++ | 4 % ~12s
|+++ | 5 % ~12s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|++++++ | 11% ~11s
|++++++ | 12% ~11s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~10s
|++++++++++ | 20% ~10s
|+++++++++++ | 21% ~09s
|++++++++++++ | 22% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s
Calculating cluster 1
| | 0 % ~calculating
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++ | 33% ~01s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++ | 67% ~00s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~49s
|++ | 2 % ~47s
|++ | 3 % ~46s
|+++ | 4 % ~02m 19s
|+++ | 5 % ~01m 59s
|++++ | 7 % ~01m 46s
|++++ | 8 % ~01m 36s
|+++++ | 9 % ~01m 29s
|+++++ | 10% ~01m 23s
|++++++ | 11% ~01m 18s
|+++++++ | 12% ~01m 14s
|+++++++ | 13% ~01m 11s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 05s
|+++++++++ | 16% ~01m 03s
|+++++++++ | 18% ~01m 01s
|++++++++++ | 19% ~59s
|++++++++++ | 20% ~57s
|+++++++++++ | 21% ~55s
|+++++++++++ | 22% ~54s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~49s
|++++++++++++++ | 26% ~48s
|++++++++++++++ | 27% ~47s
|+++++++++++++++ | 29% ~46s
|+++++++++++++++ | 30% ~45s
|++++++++++++++++ | 31% ~44s
|++++++++++++++++ | 32% ~43s
|+++++++++++++++++ | 33% ~42s
|++++++++++++++++++ | 34% ~41s
|++++++++++++++++++ | 35% ~40s
|+++++++++++++++++++ | 36% ~39s
|+++++++++++++++++++ | 37% ~38s
|++++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 40% ~36s
|+++++++++++++++++++++ | 41% ~35s
|+++++++++++++++++++++ | 42% ~35s
|++++++++++++++++++++++ | 43% ~34s
|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
|++++++++++++++++++++++++ | 46% ~31s
|++++++++++++++++++++++++ | 47% ~31s
|+++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~29s
|++++++++++++++++++++++++++ | 51% ~28s
|++++++++++++++++++++++++++ | 52% ~28s
|+++++++++++++++++++++++++++ | 53% ~27s
|+++++++++++++++++++++++++++ | 54% ~26s
|++++++++++++++++++++++++++++ | 55% ~25s
|+++++++++++++++++++++++++++++ | 56% ~25s
|+++++++++++++++++++++++++++++ | 57% ~24s
|++++++++++++++++++++++++++++++ | 58% ~23s
|++++++++++++++++++++++++++++++ | 59% ~23s
|+++++++++++++++++++++++++++++++ | 60% ~22s
|+++++++++++++++++++++++++++++++ | 62% ~21s
|++++++++++++++++++++++++++++++++ | 63% ~21s
|++++++++++++++++++++++++++++++++ | 64% ~20s
|+++++++++++++++++++++++++++++++++ | 65% ~19s
|+++++++++++++++++++++++++++++++++ | 66% ~19s
|++++++++++++++++++++++++++++++++++ | 67% ~18s
|+++++++++++++++++++++++++++++++++++ | 68% ~17s
|+++++++++++++++++++++++++++++++++++ | 69% ~17s
|++++++++++++++++++++++++++++++++++++ | 70% ~16s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|+++++++++++++++++++++++++++++++++++++ | 73% ~15s
|+++++++++++++++++++++++++++++++++++++ | 74% ~14s
|++++++++++++++++++++++++++++++++++++++ | 75% ~14s
|++++++++++++++++++++++++++++++++++++++ | 76% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=53s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~18s
|++ | 2 % ~20s
|++ | 3 % ~19s
|+++ | 4 % ~18s
|+++ | 5 % ~17s
|++++ | 6 % ~17s
|++++ | 7 % ~17s
|+++++ | 8 % ~17s
|+++++ | 9 % ~17s
|++++++ | 10% ~17s
|++++++ | 11% ~17s
|+++++++ | 12% ~17s
|+++++++ | 13% ~16s
|++++++++ | 14% ~16s
|++++++++ | 15% ~16s
|+++++++++ | 16% ~16s
|+++++++++ | 17% ~15s
|++++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|+++++++++++ | 20% ~15s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~12s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|++++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|++++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~09s
|+++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~54s
|++ | 2 % ~52s
|++ | 3 % ~51s
|+++ | 5 % ~50s
|+++ | 6 % ~49s
|++++ | 7 % ~49s
|+++++ | 8 % ~48s
|+++++ | 9 % ~47s
|++++++ | 10% ~47s
|++++++ | 11% ~46s
|+++++++ | 13% ~45s
|+++++++ | 14% ~45s
|++++++++ | 15% ~44s
|+++++++++ | 16% ~43s
|+++++++++ | 17% ~43s
|++++++++++ | 18% ~42s
|++++++++++ | 20% ~42s
|+++++++++++ | 21% ~41s
|+++++++++++ | 22% ~40s
|++++++++++++ | 23% ~39s
|+++++++++++++ | 24% ~39s
|+++++++++++++ | 25% ~38s
|++++++++++++++ | 26% ~38s
|++++++++++++++ | 28% ~37s
|+++++++++++++++ | 29% ~36s
|+++++++++++++++ | 30% ~36s
|++++++++++++++++ | 31% ~35s
|+++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|++++++++++++++++++ | 34% ~33s
|++++++++++++++++++ | 36% ~33s
|+++++++++++++++++++ | 37% ~32s
|+++++++++++++++++++ | 38% ~32s
|++++++++++++++++++++ | 39% ~31s
|+++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~30s
|++++++++++++++++++++++ | 43% ~29s
|++++++++++++++++++++++ | 44% ~29s
|+++++++++++++++++++++++ | 45% ~28s
|+++++++++++++++++++++++ | 46% ~27s
|++++++++++++++++++++++++ | 47% ~27s
|+++++++++++++++++++++++++ | 48% ~26s
|+++++++++++++++++++++++++ | 49% ~26s
|++++++++++++++++++++++++++ | 51% ~25s
|++++++++++++++++++++++++++ | 52% ~24s
|+++++++++++++++++++++++++++ | 53% ~24s
|++++++++++++++++++++++++++++ | 54% ~23s
|++++++++++++++++++++++++++++ | 55% ~23s
|+++++++++++++++++++++++++++++ | 56% ~22s
|+++++++++++++++++++++++++++++ | 57% ~22s
|++++++++++++++++++++++++++++++ | 59% ~21s
|++++++++++++++++++++++++++++++ | 60% ~21s
|+++++++++++++++++++++++++++++++ | 61% ~20s
|++++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~19s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 66% ~18s
|++++++++++++++++++++++++++++++++++ | 67% ~17s
|++++++++++++++++++++++++++++++++++ | 68% ~17s
|+++++++++++++++++++++++++++++++++++ | 69% ~16s
|++++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|+++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 74% ~14s
|++++++++++++++++++++++++++++++++++++++ | 75% ~13s
|++++++++++++++++++++++++++++++++++++++ | 76% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=55s
Calculating cluster 5
| | 0 % ~calculating
|++ | 2 % ~09s
|+++ | 4 % ~09s
|++++ | 6 % ~08s
|+++++ | 8 % ~08s
|++++++ | 10% ~08s
|+++++++ | 12% ~08s
|++++++++ | 15% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 21% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02m 25s
|++ | 2 % ~02m 20s
|++ | 3 % ~02m 16s
|+++ | 4 % ~02m 15s
|+++ | 6 % ~02m 12s
|++++ | 7 % ~02m 11s
|++++ | 8 % ~02m 10s
|+++++ | 9 % ~02m 08s
|+++++ | 10% ~02m 07s
|++++++ | 11% ~02m 06s
|+++++++ | 12% ~02m 04s
|+++++++ | 13% ~02m 02s
|++++++++ | 14% ~02m 00s
|++++++++ | 16% ~01m 59s
|+++++++++ | 17% ~01m 58s
|+++++++++ | 18% ~01m 57s
|++++++++++ | 19% ~01m 55s
|++++++++++ | 20% ~01m 54s
|+++++++++++ | 21% ~01m 53s
|++++++++++++ | 22% ~01m 52s
|++++++++++++ | 23% ~01m 51s
|+++++++++++++ | 24% ~01m 60s
|+++++++++++++ | 26% ~01m 58s
|++++++++++++++ | 27% ~01m 56s
|++++++++++++++ | 28% ~01m 54s
|+++++++++++++++ | 29% ~01m 52s
|+++++++++++++++ | 30% ~01m 50s
|++++++++++++++++ | 31% ~01m 48s
|+++++++++++++++++ | 32% ~01m 46s
|+++++++++++++++++ | 33% ~01m 44s
|++++++++++++++++++ | 34% ~01m 42s
|++++++++++++++++++ | 36% ~01m 40s
|+++++++++++++++++++ | 37% ~01m 38s
|+++++++++++++++++++ | 38% ~01m 36s
|++++++++++++++++++++ | 39% ~01m 34s
|++++++++++++++++++++ | 40% ~01m 32s
|+++++++++++++++++++++ | 41% ~01m 30s
|++++++++++++++++++++++ | 42% ~01m 28s
|++++++++++++++++++++++ | 43% ~01m 26s
|+++++++++++++++++++++++ | 44% ~01m 24s
|+++++++++++++++++++++++ | 46% ~01m 22s
|++++++++++++++++++++++++ | 47% ~01m 20s
|++++++++++++++++++++++++ | 48% ~01m 19s
|+++++++++++++++++++++++++ | 49% ~01m 17s
|+++++++++++++++++++++++++ | 50% ~01m 15s
|++++++++++++++++++++++++++ | 51% ~01m 13s
|+++++++++++++++++++++++++++ | 52% ~01m 12s
|+++++++++++++++++++++++++++ | 53% ~01m 10s
|++++++++++++++++++++++++++++ | 54% ~01m 08s
|++++++++++++++++++++++++++++ | 56% ~01m 07s
|+++++++++++++++++++++++++++++ | 57% ~01m 05s
|+++++++++++++++++++++++++++++ | 58% ~01m 03s
|++++++++++++++++++++++++++++++ | 59% ~01m 02s
|++++++++++++++++++++++++++++++ | 60% ~01m 00s
|+++++++++++++++++++++++++++++++ | 61% ~58s
|++++++++++++++++++++++++++++++++ | 62% ~57s
|++++++++++++++++++++++++++++++++ | 63% ~55s
|+++++++++++++++++++++++++++++++++ | 64% ~53s
|+++++++++++++++++++++++++++++++++ | 66% ~52s
|++++++++++++++++++++++++++++++++++ | 67% ~50s
|++++++++++++++++++++++++++++++++++ | 68% ~48s
|+++++++++++++++++++++++++++++++++++ | 69% ~47s
|+++++++++++++++++++++++++++++++++++ | 70% ~45s
|++++++++++++++++++++++++++++++++++++ | 71% ~45s
|+++++++++++++++++++++++++++++++++++++ | 72% ~43s
|+++++++++++++++++++++++++++++++++++++ | 73% ~41s
|++++++++++++++++++++++++++++++++++++++ | 74% ~39s
|++++++++++++++++++++++++++++++++++++++ | 76% ~38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~36s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~34s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~31s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~26s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 30s
Calculating cluster 7
| | 0 % ~calculating
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++ | 14% ~02s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++ | 29% ~01s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++ | 43% ~01s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++ | 57% ~01s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~13s
|++++ | 7 % ~14s
|+++++ | 8 % ~14s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|+++++++++++++++ | 29% ~11s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|+++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s
Calculating cluster 9
| | 0 % ~calculating
|++ | 2 % ~08s
|+++ | 5 % ~08s
|++++ | 7 % ~07s
|+++++ | 9 % ~07s
|++++++ | 12% ~07s
|+++++++ | 14% ~07s
|+++++++++ | 16% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 21% ~07s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 28% ~06s
|++++++++++++++++ | 30% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 42% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|++++++++++++++++++++++++++++++ | 58% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 10
| | 0 % ~calculating
|++ | 3 % ~07s
|+++ | 5 % ~07s
|++++ | 8 % ~07s
|++++++ | 10% ~07s
|+++++++ | 13% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 18% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 26% ~06s
|+++++++++++++++ | 28% ~06s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 36% ~05s
|++++++++++++++++++++ | 38% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 44% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 56% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~16s
|++ | 3 % ~15s
|++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 13% ~14s
|+++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~13s
|+++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~13s
|+++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~12s
|+++++++++++++ | 25% ~12s
|++++++++++++++ | 27% ~12s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|+++++++++++++++++++ | 37% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~09s
|+++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s
Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~40s
|++ | 2 % ~40s
|++ | 3 % ~39s
|+++ | 4 % ~38s
|+++ | 6 % ~37s
|++++ | 7 % ~37s
|++++ | 8 % ~36s
|+++++ | 9 % ~36s
|+++++ | 10% ~36s
|++++++ | 11% ~35s
|+++++++ | 12% ~35s
|+++++++ | 13% ~34s
|++++++++ | 14% ~34s
|++++++++ | 16% ~33s
|+++++++++ | 17% ~33s
|+++++++++ | 18% ~32s
|++++++++++ | 19% ~32s
|++++++++++ | 20% ~31s
|+++++++++++ | 21% ~31s
|++++++++++++ | 22% ~30s
|++++++++++++ | 23% ~30s
|+++++++++++++ | 24% ~30s
|+++++++++++++ | 26% ~29s
|++++++++++++++ | 27% ~29s
|++++++++++++++ | 28% ~28s
|+++++++++++++++ | 29% ~28s
|+++++++++++++++ | 30% ~27s
|++++++++++++++++ | 31% ~27s
|+++++++++++++++++ | 32% ~26s
|+++++++++++++++++ | 33% ~26s
|++++++++++++++++++ | 34% ~25s
|++++++++++++++++++ | 36% ~27s
|+++++++++++++++++++ | 37% ~26s
|+++++++++++++++++++ | 38% ~26s
|++++++++++++++++++++ | 39% ~25s
|++++++++++++++++++++ | 40% ~25s
|+++++++++++++++++++++ | 41% ~24s
|++++++++++++++++++++++ | 42% ~24s
|++++++++++++++++++++++ | 43% ~23s
|+++++++++++++++++++++++ | 44% ~23s
|+++++++++++++++++++++++ | 46% ~22s
|++++++++++++++++++++++++ | 47% ~22s
|++++++++++++++++++++++++ | 48% ~21s
|+++++++++++++++++++++++++ | 49% ~21s
|+++++++++++++++++++++++++ | 50% ~20s
|++++++++++++++++++++++++++ | 51% ~20s
|+++++++++++++++++++++++++++ | 52% ~19s
|+++++++++++++++++++++++++++ | 53% ~19s
|++++++++++++++++++++++++++++ | 54% ~18s
|++++++++++++++++++++++++++++ | 56% ~18s
|+++++++++++++++++++++++++++++ | 57% ~17s
|+++++++++++++++++++++++++++++ | 58% ~17s
|++++++++++++++++++++++++++++++ | 59% ~16s
|++++++++++++++++++++++++++++++ | 60% ~16s
|+++++++++++++++++++++++++++++++ | 61% ~15s
|++++++++++++++++++++++++++++++++ | 62% ~15s
|++++++++++++++++++++++++++++++++ | 63% ~14s
|+++++++++++++++++++++++++++++++++ | 64% ~14s
|+++++++++++++++++++++++++++++++++ | 66% ~14s
|++++++++++++++++++++++++++++++++++ | 67% ~13s
|++++++++++++++++++++++++++++++++++ | 68% ~13s
|+++++++++++++++++++++++++++++++++++ | 69% ~12s
|+++++++++++++++++++++++++++++++++++ | 70% ~12s
|++++++++++++++++++++++++++++++++++++ | 71% ~11s
|+++++++++++++++++++++++++++++++++++++ | 72% ~11s
|+++++++++++++++++++++++++++++++++++++ | 73% ~10s
|++++++++++++++++++++++++++++++++++++++ | 74% ~10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=39s
Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~14m 32s
|++ | 2 % ~11m 36s
|++ | 3 % ~10m 32s
|+++ | 4 % ~09m 57s
|+++ | 5 % ~09m 35s
|++++ | 6 % ~09m 16s
|++++ | 7 % ~09m 02s
|+++++ | 8 % ~08m 49s
|+++++ | 9 % ~08m 38s
|++++++ | 10% ~08m 29s
|++++++ | 11% ~08m 23s
|+++++++ | 12% ~08m 17s
|+++++++ | 13% ~08m 11s
|++++++++ | 14% ~08m 03s
|++++++++ | 15% ~08m 13s
|+++++++++ | 16% ~08m 05s
|+++++++++ | 18% ~07m 57s
|++++++++++ | 19% ~07m 50s
|++++++++++ | 20% ~07m 42s
|+++++++++++ | 21% ~07m 34s
|+++++++++++ | 22% ~07m 26s
|++++++++++++ | 23% ~07m 19s
|++++++++++++ | 24% ~07m 12s
|+++++++++++++ | 25% ~07m 05s
|+++++++++++++ | 26% ~06m 60s
|++++++++++++++ | 27% ~06m 54s
|++++++++++++++ | 28% ~06m 48s
|+++++++++++++++ | 29% ~06m 41s
|+++++++++++++++ | 30% ~06m 42s
|++++++++++++++++ | 31% ~06m 35s
|++++++++++++++++ | 32% ~06m 29s
|+++++++++++++++++ | 33% ~06m 22s
|++++++++++++++++++ | 34% ~06m 15s
|++++++++++++++++++ | 35% ~06m 09s
|+++++++++++++++++++ | 36% ~06m 02s
|+++++++++++++++++++ | 37% ~05m 56s
|++++++++++++++++++++ | 38% ~05m 50s
|++++++++++++++++++++ | 39% ~05m 44s
|+++++++++++++++++++++ | 40% ~05m 38s
|+++++++++++++++++++++ | 41% ~05m 32s
|++++++++++++++++++++++ | 42% ~05m 26s
|++++++++++++++++++++++ | 43% ~05m 24s
|+++++++++++++++++++++++ | 44% ~05m 18s
|+++++++++++++++++++++++ | 45% ~05m 11s
|++++++++++++++++++++++++ | 46% ~05m 05s
|++++++++++++++++++++++++ | 47% ~04m 59s
|+++++++++++++++++++++++++ | 48% ~04m 52s
|+++++++++++++++++++++++++ | 49% ~04m 46s
|++++++++++++++++++++++++++ | 51% ~04m 40s
|++++++++++++++++++++++++++ | 52% ~04m 34s
|+++++++++++++++++++++++++++ | 53% ~04m 28s
|+++++++++++++++++++++++++++ | 54% ~04m 22s
|++++++++++++++++++++++++++++ | 55% ~04m 16s
|++++++++++++++++++++++++++++ | 56% ~04m 10s
|+++++++++++++++++++++++++++++ | 57% ~04m 04s
|+++++++++++++++++++++++++++++ | 58% ~04m 00s
|++++++++++++++++++++++++++++++ | 59% ~03m 54s
|++++++++++++++++++++++++++++++ | 60% ~03m 48s
|+++++++++++++++++++++++++++++++ | 61% ~03m 42s
|+++++++++++++++++++++++++++++++ | 62% ~03m 36s
|++++++++++++++++++++++++++++++++ | 63% ~03m 30s
|++++++++++++++++++++++++++++++++ | 64% ~03m 24s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 18s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 12s
|++++++++++++++++++++++++++++++++++ | 67% ~03m 06s
|+++++++++++++++++++++++++++++++++++ | 68% ~03m 00s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 54s
|++++++++++++++++++++++++++++++++++++ | 70% ~02m 48s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 43s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 38s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02m 32s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02m 26s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 20s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02m 14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02m 08s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02m 02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 56s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 50s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 44s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 39s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 33s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 27s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01m 04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~58s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~52s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~46s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~41s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~35s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 24s
Calculating cluster 14
Calculating cluster 15
The following tests were not performed: When testing 14 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 15 versus all:
Cell group 1 has fewer than 3 cells
allmk_geneact_macs = allmk_geneact_macs[allmk_geneact_macs$p_val_adj<=0.05,]
write.csv(allmk_geneact_macs, file = "results/multiome/allmk_geneact_macs.csv",
col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
Add motif information to the ATAC object
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters.RDS")
Determine enriched Motifs from group of regions
# Get a list of motif position frequency matrices from the JASPAR database - vertebrates
pfm = getMatrixSet(JASPAR2020, opts = list(tax_group = "vertebrates", all_versions = F))
DefaultAssay(integ_animal) = "ATAC"
# add motif information
integ_animal = AddMotifs(object = integ_animal, pfm = pfm, assay = "ATAC",
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)
Building motif matrix
Finding motif positions
Creating Motif object
DefaultAssay(integ_macs_animal) = "ATAC"
# add motif information
integ_macs_animal = AddMotifs(object = integ_macs_animal, pfm = pfm, assay = "ATAC",
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)
Building motif matrix
Finding motif positions
Creating Motif object
Plot top enriched motifs
MotifPlot(integ_animal, motifs = head(rownames(enr_motifs_atac)))
MotifPlot(integ_macs_animal, motifs = head(rownames(enr_motifs_macs)))
Compute motif activity
MotifPlot(integ_animal, motifs = head(rownames(enr_motifs_atac)))
MotifPlot(integ_macs_animal, motifs = head(rownames(enr_motifs_macs)))
DE motifs by activity
integ_animal = RunChromVAR(integ_animal,
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)
Computing GC bias per region
Selecting background regions
Computing deviations from background
Constructing chromVAR assay
DefaultAssay(integ_animal) = 'chromvar'
# look at the activity of Mef2c
FeaturePlot(object = integ_animal, features = "MA1099.2", min.cutoff = 'q10',
max.cutoff = 'q90', pt.size = 0.1)
integ_macs_animal = RunChromVAR(integ_macs_animal,
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)
Computing GC bias per region
Selecting background regions
Computing deviations from background
Constructing chromVAR assay
DefaultAssay(integ_macs_animal) = 'chromvar'
# look at the activity of Mef2c
FeaturePlot(object = integ_macs_animal, features = "MA1099.2", min.cutoff = 'q10',
max.cutoff = 'q90', pt.size = 0.1)
TF footprinting (needs access to fragments) - VERY SLOW, do for relevant ones only
DefaultAssay(integ_animal) = 'chromvar'
diff_motifact = FindAllMarkers(object = integ_animal, min.pct = 0.2, test.use = 'LR',
latent.vars = 'nCount_ATAC', verbose = F)
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredThe following tests were not performed: When testing 15 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 16 versus all:
Cell group 1 has fewer than 3 cells
diff_motifact = diff_motifact[diff_motifact$p_val_adj<=0.05,]
diff_motifact$name = unlist(integ_animal@assays$ATAC@motifs@motif.names[diff_motifact$gene])
write.csv(diff_motifact, file = "results/multiome/diff_motifact_atac.csv",
col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
MotifPlot(integ_animal, motifs = head(diff_motifact$gene), assay = 'ATAC')
DefaultAssay(integ_macs_animal) = 'chromvar'
diff_motifact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, test.use = 'LR',
latent.vars = 'nCount_ATAC', verbose = F)
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredThe following tests were not performed: When testing 14 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 15 versus all:
Cell group 1 has fewer than 3 cells
diff_motifact_macs = diff_motifact_macs[diff_motifact_macs$p_val_adj<=0.05,]
diff_motifact_macs$name = unlist(integ_macs_animal@assays$ATAC@motifs@motif.names[diff_motifact_macs$gene])
write.csv(diff_motifact_macs, file = "results/multiome/diff_motifact_macs.csv",
col.names = T, row.names = F, quote = F)
attempt to set 'col.names' ignored
MotifPlot(integ_macs_animal, motifs = head(diff_motifact_macs$gene), assay = 'ATAC')
Save object with annotations and gene activities
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters_motifs.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")
Load ATAC data again
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters_motifs.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")
Load all snRNA-seq (multiome and other)
rna_dat = readRDS("data/expression/axolotl_reclust/all_nuclei_clustered_highlevel_anno.RDS")
Filter non-multiome data
rna_dat = rna_dat[,rna_dat$chem=="multiome"]
Reformat cell names to match those in ATAC
rna_dat = rna_dat[,rna_dat$chem=="multiome"]
Overlap in cells with ATAC
newcellnames = colnames(rna_dat)
newcellnames = gsub("-1_1", "-a1-1", newcellnames)
newcellnames = gsub("-1_2", "-a1-2", newcellnames)
newcellnames = gsub("-1_3", "-a3-1", newcellnames)
newcellnames = gsub("-1_4", "-a3-2", newcellnames)
rna_dat = RenameCells(rna_dat, new.names = newcellnames)
Subset and SCT normalise
par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_animal)))
title("all")
for(s in c("a1-1","a1-2","a3-1","a3-2")){
gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))],
"ATAC" = colnames(integ_animal)[grepl(s,colnames(integ_animal))]))
title(s)
}
par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_macs_animal)))
title("all")
for(s in c("a1-1","a1-2","a3-1","a3-2")){
gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))],
"ATAC" = colnames(integ_macs_animal)[grepl(s,colnames(integ_macs_animal))]))
title(s)
}
Get PCA and UMAP for RNA
sub_rna_atac = rna_dat[,colnames(rna_dat) %in% colnames(integ_animal)]
sub_rna_atac = NormalizeData(sub_rna_atac)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
sub_rna_atac = FindVariableFeatures(sub_rna_atac)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
sub_rna_atac = ScaleData(sub_rna_atac, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))
Regressing out nCount_RNA, nFeature_RNA, percent.mt
|
| | 0%
|
|= | 0%
|
|= | 1%
|
|== | 1%
|
|=== | 2%
|
|==== | 2%
|
|==== | 3%
|
|===== | 3%
|
|====== | 3%
|
|====== | 4%
|
|======= | 4%
|
|======== | 4%
|
|======== | 5%
|
|========= | 5%
|
|========= | 6%
|
|========== | 6%
|
|=========== | 6%
|
|=========== | 7%
|
|============ | 7%
|
|============= | 7%
|
|============= | 8%
|
|============== | 8%
|
|=============== | 9%
|
|================ | 9%
|
|================ | 10%
|
|================= | 10%
|
|================== | 10%
|
|================== | 11%
|
|=================== | 11%
|
|==================== | 12%
|
|===================== | 12%
|
|===================== | 13%
|
|====================== | 13%
|
|======================= | 13%
|
|======================= | 14%
|
|======================== | 14%
|
|========================= | 14%
|
|========================= | 15%
|
|========================== | 15%
|
|========================== | 16%
|
|=========================== | 16%
|
|============================ | 16%
|
|============================ | 17%
|
|============================= | 17%
|
|============================== | 17%
|
|============================== | 18%
|
|=============================== | 18%
|
|================================ | 19%
|
|================================= | 19%
|
|================================= | 20%
|
|================================== | 20%
|
|=================================== | 20%
|
|=================================== | 21%
|
|==================================== | 21%
|
|===================================== | 22%
|
|====================================== | 22%
|
|====================================== | 23%
|
|======================================= | 23%
|
|======================================== | 23%
|
|======================================== | 24%
|
|========================================= | 24%
|
|========================================== | 24%
|
|========================================== | 25%
|
|=========================================== | 25%
|
|=========================================== | 26%
|
|============================================ | 26%
|
|============================================= | 26%
|
|============================================= | 27%
|
|============================================== | 27%
|
|=============================================== | 27%
|
|=============================================== | 28%
|
|================================================ | 28%
|
|================================================= | 29%
|
|================================================== | 29%
|
|================================================== | 30%
|
|=================================================== | 30%
|
|==================================================== | 30%
|
|==================================================== | 31%
|
|===================================================== | 31%
|
|====================================================== | 32%
|
|======================================================= | 32%
|
|======================================================= | 33%
|
|======================================================== | 33%
|
|========================================================= | 33%
|
|========================================================= | 34%
|
|========================================================== | 34%
|
|=========================================================== | 34%
|
|=========================================================== | 35%
|
|============================================================ | 35%
|
|============================================================ | 36%
|
|============================================================= | 36%
|
|============================================================== | 36%
|
|============================================================== | 37%
|
|=============================================================== | 37%
|
|================================================================ | 37%
|
|================================================================ | 38%
|
|================================================================= | 38%
|
|================================================================== | 39%
|
|=================================================================== | 39%
|
|=================================================================== | 40%
|
|==================================================================== | 40%
|
|===================================================================== | 40%
|
|===================================================================== | 41%
|
|====================================================================== | 41%
|
|======================================================================= | 42%
|
|======================================================================== | 42%
|
|======================================================================== | 43%
|
|========================================================================= | 43%
|
|========================================================================== | 43%
|
|========================================================================== | 44%
|
|=========================================================================== | 44%
|
|============================================================================ | 44%
|
|============================================================================ | 45%
|
|============================================================================= | 45%
|
|============================================================================= | 46%
|
|============================================================================== | 46%
|
|=============================================================================== | 46%
|
|=============================================================================== | 47%
|
|================================================================================ | 47%
|
|================================================================================= | 47%
|
|================================================================================= | 48%
|
|================================================================================== | 48%
|
|=================================================================================== | 49%
|
|==================================================================================== | 49%
|
|==================================================================================== | 50%
|
|===================================================================================== | 50%
|
|====================================================================================== | 50%
|
|====================================================================================== | 51%
|
|======================================================================================= | 51%
|
|======================================================================================== | 52%
|
|========================================================================================= | 52%
|
|========================================================================================= | 53%
|
|========================================================================================== | 53%
|
|=========================================================================================== | 53%
|
|=========================================================================================== | 54%
|
|============================================================================================ | 54%
|
|============================================================================================= | 54%
|
|============================================================================================= | 55%
|
|============================================================================================== | 55%
|
|============================================================================================== | 56%
|
|=============================================================================================== | 56%
|
|================================================================================================ | 56%
|
|================================================================================================ | 57%
|
|================================================================================================= | 57%
|
|================================================================================================== | 57%
|
|================================================================================================== | 58%
|
|=================================================================================================== | 58%
|
|==================================================================================================== | 59%
|
|===================================================================================================== | 59%
|
|===================================================================================================== | 60%
|
|====================================================================================================== | 60%
|
|======================================================================================================= | 60%
|
|======================================================================================================= | 61%
|
|======================================================================================================== | 61%
|
|========================================================================================================= | 62%
|
|========================================================================================================== | 62%
|
|========================================================================================================== | 63%
|
|=========================================================================================================== | 63%
|
|============================================================================================================ | 63%
|
|============================================================================================================ | 64%
|
|============================================================================================================= | 64%
|
|============================================================================================================== | 64%
|
|============================================================================================================== | 65%
|
|=============================================================================================================== | 65%
|
|=============================================================================================================== | 66%
|
|================================================================================================================ | 66%
|
|================================================================================================================= | 66%
|
|================================================================================================================= | 67%
|
|================================================================================================================== | 67%
|
|=================================================================================================================== | 67%
|
|=================================================================================================================== | 68%
|
|==================================================================================================================== | 68%
|
|===================================================================================================================== | 69%
|
|====================================================================================================================== | 69%
|
|====================================================================================================================== | 70%
|
|======================================================================================================================= | 70%
|
|======================================================================================================================== | 70%
|
|======================================================================================================================== | 71%
|
|========================================================================================================================= | 71%
|
|========================================================================================================================== | 72%
|
|=========================================================================================================================== | 72%
|
|=========================================================================================================================== | 73%
|
|============================================================================================================================ | 73%
|
|============================================================================================================================= | 73%
|
|============================================================================================================================= | 74%
|
|============================================================================================================================== | 74%
|
|=============================================================================================================================== | 74%
|
|=============================================================================================================================== | 75%
|
|================================================================================================================================ | 75%
|
|================================================================================================================================ | 76%
|
|================================================================================================================================= | 76%
|
|================================================================================================================================== | 76%
|
|================================================================================================================================== | 77%
|
|=================================================================================================================================== | 77%
|
|==================================================================================================================================== | 77%
|
|==================================================================================================================================== | 78%
|
|===================================================================================================================================== | 78%
|
|====================================================================================================================================== | 79%
|
|======================================================================================================================================= | 79%
|
|======================================================================================================================================= | 80%
|
|======================================================================================================================================== | 80%
|
|========================================================================================================================================= | 80%
|
|========================================================================================================================================= | 81%
|
|========================================================================================================================================== | 81%
|
|=========================================================================================================================================== | 82%
|
|============================================================================================================================================ | 82%
|
|============================================================================================================================================ | 83%
|
|============================================================================================================================================= | 83%
|
|============================================================================================================================================== | 83%
|
|============================================================================================================================================== | 84%
|
|=============================================================================================================================================== | 84%
|
|================================================================================================================================================ | 84%
|
|================================================================================================================================================ | 85%
|
|================================================================================================================================================= | 85%
|
|================================================================================================================================================= | 86%
|
|================================================================================================================================================== | 86%
|
|=================================================================================================================================================== | 86%
|
|=================================================================================================================================================== | 87%
|
|==================================================================================================================================================== | 87%
|
|===================================================================================================================================================== | 87%
|
|===================================================================================================================================================== | 88%
|
|====================================================================================================================================================== | 88%
|
|======================================================================================================================================================= | 89%
|
|======================================================================================================================================================== | 89%
|
|======================================================================================================================================================== | 90%
|
|========================================================================================================================================================= | 90%
|
|========================================================================================================================================================== | 90%
|
|========================================================================================================================================================== | 91%
|
|=========================================================================================================================================================== | 91%
|
|============================================================================================================================================================ | 92%
|
|============================================================================================================================================================= | 92%
|
|============================================================================================================================================================= | 93%
|
|============================================================================================================================================================== | 93%
|
|=============================================================================================================================================================== | 93%
|
|=============================================================================================================================================================== | 94%
|
|================================================================================================================================================================ | 94%
|
|================================================================================================================================================================= | 94%
|
|================================================================================================================================================================= | 95%
|
|================================================================================================================================================================== | 95%
|
|================================================================================================================================================================== | 96%
|
|=================================================================================================================================================================== | 96%
|
|==================================================================================================================================================================== | 96%
|
|==================================================================================================================================================================== | 97%
|
|===================================================================================================================================================================== | 97%
|
|====================================================================================================================================================================== | 97%
|
|====================================================================================================================================================================== | 98%
|
|======================================================================================================================================================================= | 98%
|
|======================================================================================================================================================================== | 99%
|
|========================================================================================================================================================================= | 99%
|
|========================================================================================================================================================================= | 100%
|
|==========================================================================================================================================================================| 100%
Centering and scaling data matrix
|
| | 0%
|
|===================================================================================== | 50%
|
|==========================================================================================================================================================================| 100%
sub_rna_macs = rna_dat[,colnames(rna_dat) %in% colnames(integ_macs_animal)]
sub_rna_macs = NormalizeData(sub_rna_macs)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
sub_rna_macs = FindVariableFeatures(sub_rna_macs)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
sub_rna_macs = ScaleData(sub_rna_macs, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))
Regressing out nCount_RNA, nFeature_RNA, percent.mt
|
| | 0%
|
|= | 0%
|
|= | 1%
|
|== | 1%
|
|=== | 2%
|
|==== | 2%
|
|==== | 3%
|
|===== | 3%
|
|====== | 3%
|
|====== | 4%
|
|======= | 4%
|
|======== | 4%
|
|======== | 5%
|
|========= | 5%
|
|========= | 6%
|
|========== | 6%
|
|=========== | 6%
|
|=========== | 7%
|
|============ | 7%
|
|============= | 7%
|
|============= | 8%
|
|============== | 8%
|
|=============== | 9%
|
|================ | 9%
|
|================ | 10%
|
|================= | 10%
|
|================== | 10%
|
|================== | 11%
|
|=================== | 11%
|
|==================== | 12%
|
|===================== | 12%
|
|===================== | 13%
|
|====================== | 13%
|
|======================= | 13%
|
|======================= | 14%
|
|======================== | 14%
|
|========================= | 14%
|
|========================= | 15%
|
|========================== | 15%
|
|========================== | 16%
|
|=========================== | 16%
|
|============================ | 16%
|
|============================ | 17%
|
|============================= | 17%
|
|============================== | 17%
|
|============================== | 18%
|
|=============================== | 18%
|
|================================ | 19%
|
|================================= | 19%
|
|================================= | 20%
|
|================================== | 20%
|
|=================================== | 20%
|
|=================================== | 21%
|
|==================================== | 21%
|
|===================================== | 22%
|
|====================================== | 22%
|
|====================================== | 23%
|
|======================================= | 23%
|
|======================================== | 23%
|
|======================================== | 24%
|
|========================================= | 24%
|
|========================================== | 24%
|
|========================================== | 25%
|
|=========================================== | 25%
|
|=========================================== | 26%
|
|============================================ | 26%
|
|============================================= | 26%
|
|============================================= | 27%
|
|============================================== | 27%
|
|=============================================== | 27%
|
|=============================================== | 28%
|
|================================================ | 28%
|
|================================================= | 29%
|
|================================================== | 29%
|
|================================================== | 30%
|
|=================================================== | 30%
|
|==================================================== | 30%
|
|==================================================== | 31%
|
|===================================================== | 31%
|
|====================================================== | 32%
|
|======================================================= | 32%
|
|======================================================= | 33%
|
|======================================================== | 33%
|
|========================================================= | 33%
|
|========================================================= | 34%
|
|========================================================== | 34%
|
|=========================================================== | 34%
|
|=========================================================== | 35%
|
|============================================================ | 35%
|
|============================================================ | 36%
|
|============================================================= | 36%
|
|============================================================== | 36%
|
|============================================================== | 37%
|
|=============================================================== | 37%
|
|================================================================ | 37%
|
|================================================================ | 38%
|
|================================================================= | 38%
|
|================================================================== | 39%
|
|=================================================================== | 39%
|
|=================================================================== | 40%
|
|==================================================================== | 40%
|
|===================================================================== | 40%
|
|===================================================================== | 41%
|
|====================================================================== | 41%
|
|======================================================================= | 42%
|
|======================================================================== | 42%
|
|======================================================================== | 43%
|
|========================================================================= | 43%
|
|========================================================================== | 43%
|
|========================================================================== | 44%
|
|=========================================================================== | 44%
|
|============================================================================ | 44%
|
|============================================================================ | 45%
|
|============================================================================= | 45%
|
|============================================================================= | 46%
|
|============================================================================== | 46%
|
|=============================================================================== | 46%
|
|=============================================================================== | 47%
|
|================================================================================ | 47%
|
|================================================================================= | 47%
|
|================================================================================= | 48%
|
|================================================================================== | 48%
|
|=================================================================================== | 49%
|
|==================================================================================== | 49%
|
|==================================================================================== | 50%
|
|===================================================================================== | 50%
|
|====================================================================================== | 50%
|
|====================================================================================== | 51%
|
|======================================================================================= | 51%
|
|======================================================================================== | 52%
|
|========================================================================================= | 52%
|
|========================================================================================= | 53%
|
|========================================================================================== | 53%
|
|=========================================================================================== | 53%
|
|=========================================================================================== | 54%
|
|============================================================================================ | 54%
|
|============================================================================================= | 54%
|
|============================================================================================= | 55%
|
|============================================================================================== | 55%
|
|============================================================================================== | 56%
|
|=============================================================================================== | 56%
|
|================================================================================================ | 56%
|
|================================================================================================ | 57%
|
|================================================================================================= | 57%
|
|================================================================================================== | 57%
|
|================================================================================================== | 58%
|
|=================================================================================================== | 58%
|
|==================================================================================================== | 59%
|
|===================================================================================================== | 59%
|
|===================================================================================================== | 60%
|
|====================================================================================================== | 60%
|
|======================================================================================================= | 60%
|
|======================================================================================================= | 61%
|
|======================================================================================================== | 61%
|
|========================================================================================================= | 62%
|
|========================================================================================================== | 62%
|
|========================================================================================================== | 63%
|
|=========================================================================================================== | 63%
|
|============================================================================================================ | 63%
|
|============================================================================================================ | 64%
|
|============================================================================================================= | 64%
|
|============================================================================================================== | 64%
|
|============================================================================================================== | 65%
|
|=============================================================================================================== | 65%
|
|=============================================================================================================== | 66%
|
|================================================================================================================ | 66%
|
|================================================================================================================= | 66%
|
|================================================================================================================= | 67%
|
|================================================================================================================== | 67%
|
|=================================================================================================================== | 67%
|
|=================================================================================================================== | 68%
|
|==================================================================================================================== | 68%
|
|===================================================================================================================== | 69%
|
|====================================================================================================================== | 69%
|
|====================================================================================================================== | 70%
|
|======================================================================================================================= | 70%
|
|======================================================================================================================== | 70%
|
|======================================================================================================================== | 71%
|
|========================================================================================================================= | 71%
|
|========================================================================================================================== | 72%
|
|=========================================================================================================================== | 72%
|
|=========================================================================================================================== | 73%
|
|============================================================================================================================ | 73%
|
|============================================================================================================================= | 73%
|
|============================================================================================================================= | 74%
|
|============================================================================================================================== | 74%
|
|=============================================================================================================================== | 74%
|
|=============================================================================================================================== | 75%
|
|================================================================================================================================ | 75%
|
|================================================================================================================================ | 76%
|
|================================================================================================================================= | 76%
|
|================================================================================================================================== | 76%
|
|================================================================================================================================== | 77%
|
|=================================================================================================================================== | 77%
|
|==================================================================================================================================== | 77%
|
|==================================================================================================================================== | 78%
|
|===================================================================================================================================== | 78%
|
|====================================================================================================================================== | 79%
|
|======================================================================================================================================= | 79%
|
|======================================================================================================================================= | 80%
|
|======================================================================================================================================== | 80%
|
|========================================================================================================================================= | 80%
|
|========================================================================================================================================= | 81%
|
|========================================================================================================================================== | 81%
|
|=========================================================================================================================================== | 82%
|
|============================================================================================================================================ | 82%
|
|============================================================================================================================================ | 83%
|
|============================================================================================================================================= | 83%
|
|============================================================================================================================================== | 83%
|
|============================================================================================================================================== | 84%
|
|=============================================================================================================================================== | 84%
|
|================================================================================================================================================ | 84%
|
|================================================================================================================================================ | 85%
|
|================================================================================================================================================= | 85%
|
|================================================================================================================================================= | 86%
|
|================================================================================================================================================== | 86%
|
|=================================================================================================================================================== | 86%
|
|=================================================================================================================================================== | 87%
|
|==================================================================================================================================================== | 87%
|
|===================================================================================================================================================== | 87%
|
|===================================================================================================================================================== | 88%
|
|====================================================================================================================================================== | 88%
|
|======================================================================================================================================================= | 89%
|
|======================================================================================================================================================== | 89%
|
|======================================================================================================================================================== | 90%
|
|========================================================================================================================================================= | 90%
|
|========================================================================================================================================================== | 90%
|
|========================================================================================================================================================== | 91%
|
|=========================================================================================================================================================== | 91%
|
|============================================================================================================================================================ | 92%
|
|============================================================================================================================================================= | 92%
|
|============================================================================================================================================================= | 93%
|
|============================================================================================================================================================== | 93%
|
|=============================================================================================================================================================== | 93%
|
|=============================================================================================================================================================== | 94%
|
|================================================================================================================================================================ | 94%
|
|================================================================================================================================================================= | 94%
|
|================================================================================================================================================================= | 95%
|
|================================================================================================================================================================== | 95%
|
|================================================================================================================================================================== | 96%
|
|=================================================================================================================================================================== | 96%
|
|==================================================================================================================================================================== | 96%
|
|==================================================================================================================================================================== | 97%
|
|===================================================================================================================================================================== | 97%
|
|====================================================================================================================================================================== | 97%
|
|====================================================================================================================================================================== | 98%
|
|======================================================================================================================================================================= | 98%
|
|======================================================================================================================================================================== | 99%
|
|========================================================================================================================================================================= | 99%
|
|========================================================================================================================================================================= | 100%
|
|==========================================================================================================================================================================| 100%
Centering and scaling data matrix
|
| | 0%
|
|===================================================================================== | 50%
|
|==========================================================================================================================================================================| 100%
Subset ATAC data
sub_rna_atac = RunPCA(sub_rna_atac, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_atac, ndims = 50)
sub_rna_atac = RunUMAP(sub_rna_atac, reduction = "pca", dims = 1:40, verbose = F)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
Warning: Cannot add objects with duplicate keys (offending key: UMAP_), setting key to 'umap_'
sub_rna_macs = RunPCA(sub_rna_macs, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_macs, ndims = 50)
sub_rna_macs = RunUMAP(sub_rna_macs, reduction = "pca", dims = 1:40, verbose = F)
Warning: Cannot add objects with duplicate keys (offending key: UMAP_), setting key to 'umap_'
Processing ATAC
atac_multiome = integ_animal[,colnames(integ_animal) %in% colnames(sub_rna_atac)]
macs_multiome = integ_macs_animal[,colnames(integ_macs_animal) %in% colnames(sub_rna_macs)]
Processing ATAC-seq by animal
Integrate based on each animal - CellRanger peaks
multiome_atac_animal = SplitObject(atac_multiome, split.by = "animal")
for(n in names(multiome_atac_animal)){
multiome_atac_animal[[n]] = RunTFIDF(multiome_atac_animal[[n]])
multiome_atac_animal[[n]] = FindTopFeatures(multiome_atac_animal[[n]], min.cutoff = "q0")
multiome_atac_animal[[n]] = RunSVD(multiome_atac_animal[[n]])
}
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
DepthCor(multiome_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_atac_animal$a3, reduction = "lsi", n = 30)
multiome_macs_animal = SplitObject(macs_multiome, split.by = "animal")
for(n in names(multiome_macs_animal)){
multiome_macs_animal[[n]] = RunTFIDF(multiome_macs_animal[[n]])
multiome_macs_animal[[n]] = FindTopFeatures(multiome_macs_animal[[n]], min.cutoff = "q0")
multiome_macs_animal[[n]] = RunSVD(multiome_macs_animal[[n]])
}
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
DepthCor(multiome_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_macs_animal$a3, reduction = "lsi", n = 30)
Integrate based on each animal - MACS peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_atac_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(multiome_atac_animal))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s
Finding all pairwise anchors
| | 0 % ~calculating
Warning: No filtering performed if passing to data rather than counts
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 803 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 22s
# integrate LSI embeddings
mome_atac = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
reductions = atac_multiome[["lsi"]],
new.reduction.name = "integrated_lsi")
Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(mome_atac, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
mome_atac = RunUMAP(mome_atac, reduction = "integrated_lsi", dims = c(2:4,6:30), verbose = F)
DimPlot(mome_atac, group.by = "dataset")
FeaturePlot(object = mome_atac, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Add RNA to ATAC
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_macs_animal,
reduction = "rlsi", dims = c(2:3,6:30),
anchor.features = rownames(multiome_macs_animal))
Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
Finding all pairwise anchors
| | 0 % ~calculating
Warning: No filtering performed if passing to data rather than counts
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 537 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 37s
# integrate LSI embeddings
mome_macs = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
reductions = macs_multiome[["lsi"]],
new.reduction.name = "integrated_lsi")
Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
DepthCor(mome_macs, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
mome_macs = RunUMAP(mome_macs, reduction = "integrated_lsi", dims = c(2:3,5:30), verbose = F)
DimPlot(mome_macs, group.by = "dataset")
FeaturePlot(object = mome_macs, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Save multiome data
mome_atac[["RNA"]] = sub_rna_atac@assays$RNA
mome_atac@reductions$pca = sub_rna_atac@reductions$pca
mome_macs[["RNA"]] = sub_rna_macs@assays$RNA
mome_macs@reductions$pca = sub_rna_macs@reductions$pca
Plot ATAC with regions and cellclusters
saveRDS(mome_atac, file = "data/processed/multiome/multiome_integATAC_SCT.RDS")
Warning in gzfile(file, mode) :
cannot open compressed file 'data/processed/multiome/multiome_integATAC_SCT.RDS', probable reason 'No such file or directory'
Error in gzfile(file, mode) : cannot open the connection
Load multiome data
pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf", height = 3, width = 3.2)
Error in pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf", :
cannot open file 'results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf'
Prepare segregated peaks. This is done using the annotation based on the original GTF file
mome_atac_SCT = readRDS(file = "data/processed/multiome/multiome_integATAC_SCT.RDS")
mome_macs_SCT = readRDS(file = "data/processed/multiome/multiome_integMACS_SCT.RDS")
Multiome factor analysis - CellRanger peaks
# CellRanger peaks
DefaultAssay(mome_atac_SCT) = "ATAC"
transc = Annotation(mome_atac_SCT)[Annotation(mome_atac_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1) # restrict fixes issues with negative genome coordinates
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)
closest_f = ClosestFeature(mome_atac_SCT, regions = rownames(mome_atac_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)
for(tt in unique(closest_f$type)){
p = closest_f$query_region[closest_f$type==tt]
i = paste0("ATAC_", tt)
mome_atac_SCT[[i]] = CreateChromatinAssay(mome_atac_SCT@assays$ATAC@counts[p,])
mome_atac_SCT = RunTFIDF(mome_atac_SCT, assay = i)
mome_atac_SCT = FindTopFeatures(mome_atac_SCT, assay=i, min.cutoff = 300)
print(length(mome_atac_SCT[[i]]@var.features))
}
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_distal_ to atacdistal_Performing TF-IDF normalization
Some features contain 0 total counts
[1] 10072
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_genebody_ to atacgenebody_Performing TF-IDF normalization
Some features contain 0 total counts
[1] 18224
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_promoter_ to atacpromoter_Performing TF-IDF normalization
Some features contain 0 total counts
[1] 7039
# MACS3 peaks
DefaultAssay(mome_macs_SCT) = "ATAC"
transc = Annotation(mome_macs_SCT)[Annotation(mome_macs_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1)
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)
closest_f = ClosestFeature(mome_macs_SCT, regions = rownames(mome_macs_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)
for(tt in unique(closest_f$type)){
p = closest_f$query_region[closest_f$type==tt]
i = paste0("ATAC_", tt)
mome_macs_SCT[[i]] = CreateChromatinAssay(mome_macs_SCT@assays$ATAC@counts[p,])
mome_macs_SCT = RunTFIDF(mome_macs_SCT, assay = i)
mome_macs_SCT = FindTopFeatures(mome_macs_SCT, assay=i, min.cutoff = 200)
print(length(mome_macs_SCT[[i]]@var.features))
}
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_promoter_ to atacpromoter_Performing TF-IDF normalization
[1] 10411
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_distal_ to atacdistal_Performing TF-IDF normalization
Some features contain 0 total counts
[1] 13009
Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_genebody_ to atacgenebody_Performing TF-IDF normalization
Some features contain 0 total counts
[1] 15321
Plotting
set.seed(1)
# create MOFA objectrun_mofa
mofa = create_mofa(mome_atac_SCT, assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))
Creating MOFA object from a Seurat object...
No features specified, using variable features from the Seurat object...
mofa
Untrained MOFA model with the following characteristics:
Number of views: 4
Views names: RNA ATAC_promoter ATAC_distal ATAC_genebody
Number of features (per view): 2000 7039 10072 18224
Number of groups: 1
Groups names: group1
Number of samples (per group): 20484
# define model options
model_opts = get_default_model_options(mofa)
model_opts$num_factors = 25
mofa = prepare_mofa(mofa,model_options = model_opts)
Some view(s) have a lot of features, it is recommended to performa more stringent feature selection before creating the MOFA object....Checking data options...
No data options specified, using default...
No training options specified, using default...
Checking model options...
# run mofa (SLOW!)
mofa = run_mofa(mofa, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa",
use_basilisk = TRUE)
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa already exists, it will be replaced
Connecting to the mofapy2 package using basilisk.
Set 'use_basilik' to FALSE if you prefer to manually set the python binary using 'reticulate'.
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='RNA' group='group1' with N=20484 samples and D=2000 features...
Successfully loaded view='ATAC_promoter' group='group1' with N=20484 samples and D=7039 features...
Successfully loaded view='ATAC_distal' group='group1' with N=20484 samples and D=10072 features...
Successfully loaded view='ATAC_genebody' group='group1' with N=20484 samples and D=18224 features...
Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (RNA): gaussian
- View 1 (ATAC_promoter): gaussian
- View 2 (ATAC_distal): gaussian
- View 3 (ATAC_genebody): gaussian
######################################
## Training the model with seed 42 ##
######################################
ELBO before training: -11499617444.44
Iteration 1: time=622.61, ELBO=-978832718.02, deltaELBO=10520784726.418 (91.48812799%), Factors=25
Iteration 2: time=630.32, ELBO=-976416737.45, deltaELBO=2415980.575 (0.02100923%), Factors=25
Iteration 3: time=637.46, ELBO=-976126405.08, deltaELBO=290332.361 (0.00252471%), Factors=25
Iteration 4: time=665.97, ELBO=-975986573.20, deltaELBO=139831.882 (0.00121597%), Factors=25
Iteration 5: time=2951.07, ELBO=-975878348.30, deltaELBO=108224.904 (0.00094112%), Factors=25
Iteration 6: time=3582.39, ELBO=-975814351.91, deltaELBO=63996.391 (0.00055651%), Factors=25
Iteration 7: time=3464.41, ELBO=-975769593.72, deltaELBO=44758.188 (0.00038921%), Factors=25
Iteration 8: time=1722.38, ELBO=-975731005.42, deltaELBO=38588.300 (0.00033556%), Factors=25
Converged!
#######################
## Training finished ##
#######################
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa already exists, it will be replaced
Saving model in /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa...
Factor(s) 2 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
saveRDS(mofa, file = "data/processed/multiome/MOFA_multiome_integATAC_SCT.RDS")
Multiome factor analysis - MACS peaks
plot_dimred(mofa, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
#plot_dimred(mofa, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05,
# dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEIS2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEX3A", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
Error in .set_colorby(object, color_by) :
'color_by' was specified but it was not recognised, please read the documentation
Plotting
set.seed(1)
# create MOFA object
mofa_macs = create_mofa(mome_macs_SCT,
assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))
Creating MOFA object from a Seurat object...
No features specified, using variable features from the Seurat object...
mofa_macs
Untrained MOFA model with the following characteristics:
Number of views: 4
Views names: RNA ATAC_promoter ATAC_distal ATAC_genebody
Number of features (per view): 2000 10411 13009 15321
Number of groups: 1
Groups names: group1
Number of samples (per group): 20735
# define model options
model_opts = get_default_model_options(mofa_macs)
model_opts$num_factors = 25
mofa_macs = prepare_mofa(mofa_macs, model_options = model_opts)
Some view(s) have a lot of features, it is recommended to performa more stringent feature selection before creating the MOFA object....Checking data options...
No data options specified, using default...
No training options specified, using default...
Checking model options...
# run mofa (SLOW!)
mofa_macs = run_mofa(mofa_macs, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs",
use_basilisk = TRUE)
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs already exists, it will be replaced
Connecting to the mofapy2 package using basilisk.
Set 'use_basilik' to FALSE if you prefer to manually set the python binary using 'reticulate'.
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='RNA' group='group1' with N=20735 samples and D=2000 features...
Successfully loaded view='ATAC_promoter' group='group1' with N=20735 samples and D=10411 features...
Successfully loaded view='ATAC_distal' group='group1' with N=20735 samples and D=13009 features...
Successfully loaded view='ATAC_genebody' group='group1' with N=20735 samples and D=15321 features...
Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (RNA): gaussian
- View 1 (ATAC_promoter): gaussian
- View 2 (ATAC_distal): gaussian
- View 3 (ATAC_genebody): gaussian
######################################
## Training the model with seed 42 ##
######################################
ELBO before training: -12660486242.68
Iteration 1: time=707.61, ELBO=-1085560945.03, deltaELBO=11574925297.658 (91.42559832%), Factors=25
Iteration 2: time=724.01, ELBO=-1082174708.87, deltaELBO=3386236.151 (0.02674649%), Factors=25
Iteration 3: time=681.47, ELBO=-1081822947.07, deltaELBO=351761.800 (0.00277842%), Factors=25
Iteration 4: time=668.89, ELBO=-1081633322.20, deltaELBO=189624.874 (0.00149777%), Factors=25
Iteration 5: time=704.67, ELBO=-1081492873.24, deltaELBO=140448.958 (0.00110935%), Factors=25
Iteration 6: time=672.82, ELBO=-1081403049.17, deltaELBO=89824.077 (0.00070948%), Factors=25
Iteration 7: time=689.92, ELBO=-1081321850.90, deltaELBO=81198.270 (0.00064135%), Factors=25
Iteration 8: time=714.67, ELBO=-1081259560.55, deltaELBO=62290.342 (0.00049201%), Factors=25
Iteration 9: time=696.50, ELBO=-1081205771.84, deltaELBO=53788.717 (0.00042486%), Factors=25
Converged!
#######################
## Training finished ##
#######################
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs already exists, it will be replaced
Saving model in /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs...
1 factors were found to explain little or no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = F)
Factor(s) 2 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
The model contains highly correlated factors (see `plot_factor_cor(MOFAobject)`).
We recommend that you train the model with less factors and that you let it train for a longer time.
saveRDS(mofa_macs, file = "data/processed/multiome/MOFA_multiome_integMACS_SCT.RDS")
Add MOFA projection multiome data
# variance explained
plot_variance_explained(mofa_macs, plot_total = TRUE)
[[1]]
[[2]]
samples_metadata(mofa_macs) = mome_macs_SCT@meta.data %>%
tibble::rownames_to_column("sample") %>%
as.data.table
# correlated technical variables
correlate_factors_with_covariates(mofa_macs,
covariates = c("nFeature_RNA","nFeature_ATAC",
"nCount_RNA","nCount_ATAC", "animal"))
There are non-numeric values in the covariates data.frame, converting to numeric...
# example factors
plot_factor(mofa_macs, factors=1, group_by = "animal", color_by = "animal")
plot_factor(mofa_macs, factors=10, group_by = "animal", color_by = "animal")
# example factor weights
plot_weights(mofa_macs, view = "RNA", factors = 1, nfeatures = 20, text_size = 4)
# select factors and run UMAP
factors = 1:get_dimensions(mofa)[["K"]]
factors = factors[c(3:6, 8, 11:get_dimensions(mofa_macs)[["K"]])]
mofa_macs = run_umap(mofa_macs, factors = factors, n_neighbors = 15, min_dist = 0.30)
# plots
plot_dimred(mofa_macs, method = "UMAP", color_by = "animal", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "nCount_RNA", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "seurat_clusters", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
# markers
plot_dimred(mofa_macs, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
Error in .set_colorby(object, color_by) :
'color_by' was specified but it was not recognised, please read the documentation
Save data
mome_macs_SCT_mofa = add_mofa_factors_to_seurat(mofa_macs, mome_macs_SCT, views = "all", factors = "all")
(1) Adding the MOFA factors to the 'reductions' slot of the default Seurat assay with the 'MOFA' key (no feature weights/loadings provided)...
No assay specified, setting assay as RNA by default.(2) Adding the UMAP representation obtained with the MOFA factors to the 'reductions' slot of the default Seurat assay using the key 'MOFAUMAP'...
No assay specified, setting assay as RNA by default.
DefaultAssay(mome_macs_SCT_mofa) = "RNA"
mome_macs_SCT_mofa@reductions$MOFA@assay.used = "RNA"
mome_macs_SCT_mofa@reductions$MOFAUMAP@assay.used = "RNA"